Social
Cloud and systems

Bringing code analysis tools to Jupyter notebooks

Based on a survey of thousands of machine learning practitioners, a new CodeGuru extension addresses common problems, such as code cell execution order, incorrect API calls, and security.

By Linghui Luo, Shubha Gargava
July 13, 2023
7 min read
Share

The computational notebook is an interactive, web-based programming interface based on the concept of a lab notebook. Users can describe the computations they’re performing — including diagrams — and embed code in the notebook, and the notebook backend will execute the code, integrating the results into the notebook layout.

Jupyter Notebook is the most popular implementation of computational notebooks, and it has become the tool of choice for data scientists. By September 2018, there were more than 2.5 million public Jupyter notebooks on GitHub, and this number has been growing rapidly.

BugBearScreenshot.large.png
Related content
How automated reasoning improves the Prime Video experience
In a pilot study, an automated code checker found about 100 possible errors, 80% of which turned out to require correction.

However, using Jupyter Notebook poses several challenges related to code maintenance and machine learning best practices. We recently surveyed 2,669 machine learning (ML) practitioners, and 33% of them mentioned that notebooks get easily cluttered due to the mix of code, documentation, and visualization. Similarly, 23% found silent bugs hard to detect, and 18% agreed that global variables are inconsistently used. Another 15% found reproduction of notebooks to be hard, and 6% had difficulty detecting and remediating security vulnerabilities within notebooks.

We are excited to share our recent launch of the Amazon CodeGuru extension for JupyterLab and SageMaker Studio. The extension seamlessly integrates with JupyterLab and SageMaker Studio, and with a single button click, it can provide users feedback and suggestions for improving their code quality and security. To learn more about how to install and use this extension, check out this user guide.

Static analysis

Traditional software development environments commonly use static-analysis tools to identify and prevent bugs and enforce coding standards, but Jupyter notebooks currently lack such tools. We on the Amazon CodeGuru team, which has developed a portfolio of code analysis tools for Amazon Web Services customers, saw a great opportunity to adapt our existing tools for notebooks and build solutions that best fit this new problem area.

Notebook-Interface.jpg
An example of how the notebook environment can integrate discussion, code, and visualizations.

We presented our initial efforts in a paper published at the 25th International Symposium on Formal Methods in March 2023. The paper reports insights from our survey and from interviews with ML practitioners to understand what specific issues need to be addressed in the notebook context. In the following, we give two examples of how our new technologies can help machine learning experts to be more productive.

Execution order

Code is embedded in computational notebooks in code cells, which can be executed in an arbitrary order and edited on the fly; that is, cells can be added, deleted, or changed after other cells have been executed.

While this flexibility is great for exploring data, it raises problems concerning reproducibility, as cells with shared variables can produce different results when running in different orders.

Jupyter code examples.png
Left: code cells executed in nonlinear order; right: code cells executed in linear order.

Once a code cell is executed, it is assigned an integer number in the square bracket on its left side. This number is called the execution count, and it indicates the cell’s position in the execution order. In the example above, when code cells are executed in nonlinear order, the variable z ends up with the value 6. However, execution count 2 is missing in the notebook file, which can happen for multiple reasons: perhaps the cell was executed and deleted afterwards, or perhaps one of the cells was executed twice. In any case, it would be hard for a second person to reproduce the same result.

debugger_architecture.png
Related content
The science behind SageMaker’s cost-saving Debugger
New tool can spot problems — such as overfitting and vanishing gradients — that prevent machine learning models from learning.

To catch problems resulting from out-of-order execution in Jupyter notebooks, we developed a hybrid approach that combines dynamic information capture and static analysis. Our tool collects dynamic information during the execution of notebooks, then converts notebook files with Python code cells into a novel Python representation that models the execution order as well as the code cells as such. Based on this model, we are able to leverage our static-analysis engine for Python and design new static-analysis rules to catch issues in notebooks.

APIs

Another common problem for notebook users is misuse of machine-learning APIs. Popular machine learning libraries such as PyTorch, TensorFlow, and Keras greatly simplify the development of AI systems. However, due to the complexity of the field, the libraries’ high level of abstraction, and the sometimes obscure conventions governing library functions, library users often misuse these APIs and inject faults into their notebooks without even knowing it.

Embedded verification code.png
Related content
How to integrate formal proofs into software development
ICSE paper presents techniques piloted by Amazon Web Services’ Automated Reasoning team.

The code below shows such a misuse. Some layers of a neural network, such as dropout layers, may behave differently during the training and evaluation of the network. PyTorch mandates explicit calls to train() and eval() to denote the start of training and evaluation, respectively. The code example is intended to load a trained model from disk and evaluate it on some test data.

However, it misses the call to eval(), as by default, every model is in the training phase. In this case, some layers will indirectly change the architecture of the network, which would make all prediction unstable; i.e., for the same input, the predictions would be different at different times.

# noncompliant case
model.load_state_dict(torch.load("model.pth"))
predicted = model.evaluate_on(test_data)

# compliant case
model.load_state_dict(torch.load("model.pth"))
model.eval()
predicted = model.evaluate_on(test_data)



        
Instabilities caused by this bug can have a serious impact. Even when the bug is found (currently, through manual code review) and fixed, the model needs to be retrained. Depending on how large the model is and how late in the development process the bug is found, this could mean a waste of thousands of hours.
The best case would be to detect the bug directly after the developer writes the code. Static analysis can help with this. In our paper, we implemented a set of static-analysis rules that automatically analyze machine learning code in Jupyter notebooks and could detect such bugs with high precision.
In experiments involving a large set of notebook files, our rules found an average of one bug per seven notebooks. This result motivates us to dive deep into bug detection in Jupyter notebooks.
Our survey identified the following issues that notebook users care about:
  • Reproducibility: People often find it difficult to reproduce results when moving notebooks between different environments. Notebook code cells are often executed in nonlinear order, which may be not reproducible. About 14% of the survey participants collaborate on notebooks with others only when models need to be pushed into production; reproducibility is even more crucial for production notebooks.
  • Correctness: People introduce silent correctness bugs without knowing it when using machine learning libraries. Silent bugs affect model outputs but do not cause program crashes, which makes them extremely hard to find. In our survey, 23% of participants confirmed this.
  • Readability: During data exploration, notebooks can easily get messy and hard to read. This hampers maintainability as well as collaboration. In our survey, 32% of participants mentioned that readability is one of the biggest difficulties in using notebooks.
  • Performance: It is time- and memory-consuming to train big models. People want help to make both training and the runtime execution of their code more efficient.
  • Security: In our survey, 34% of participants said that security awareness among ML practitioners is low and that there is a consequent need for security scanning. Because notebooks often rely on external code and data, they can be vulnerable to code injection and data-poisoning attacks (manipulating machine learning models).
These findings pointed us toward the kinds of issues that our new analysis rules should address. During the rule sourcing and specification phase, we asked ML experts for feedback on the usefulness of the rules as well as examples of compliant and noncompliant cases to illustrate the rules. After developing the rules, we invited a group of ML experts to evaluate our tools on real-world notebooks. We used their feedback to improve the accuracy of the rules. 
The newly launched Amazon CodeGuru extension for JupyterLab and SageMaker Studio enables the enforcement of code quality and security in computational notebooks to “shift left”, or move earlier in the development process. Users can now detect security vulnerabilities — such as injection flaws, data leaks, weak cryptography, and missing encryption — within notebook cells, along with other common issues that affect the readability, reproducibility, and correctness of the computations performed by notebooks.
Acknowledgements: Martin Schäf, Omer Tripp

    



                            
                        

                    
                


                
                    

                        

    

    

        Research areas
    


    
        
    



                    

                

                
                    

                        

    

    

        Tags
    


    
        
    



                    

                

                
                    

                        
                    

                

                
                    
About the Author

                    

                        
                            
    

        

            
                

                    Linghui Luo
                

            
            
            
                

                    Linghui Luo is an applied scientist with Amazon Web Services AI.
                

            
        

    



                        
                            
    

        

            
                

                    Shubha Gargava
                

            
            
            
                

                    Shubha Gargava is a senior user-experience designer with Amazon Web Services' CodeGuru.
                

            
        

    



                        
                    

                
            

        


        
    

    
    



    

        
Related content

        
    




    
        
    

    



    
    



    

        
Work with us
See more jobs
        
            See more jobs
        
    




    
        

            

                
                    

                        

    

    

        
            

                Applied Scientist, Amzn Shipping-Prd & Tech
            

        

        

            
                
                    

                
            

            

            

            

            
                
IN, HR, Gurugram

            
        


        
    
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Data Scientist, SAMBA
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, NJ, Newark

            
        


        
    
At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE We are seeking a Data Scientist to own our causal inference infrastructure and drive sophisticated modeling that measures the incremental impact of business decisions. This role requires deep expertise in advanced causal inference methodologies—including synthetic control methods, Synthetic Difference-in-Differences (SDID), and Bayesian approaches—to design rigorous experiments, estimate long-term customer behavior effects, and translate complex analytical results into clear business recommendations. You will own the development and continuous improvement of these causal inference models while being responsible for machine learning operations at scale to ensure our organization makes data-driven decisions with confidence. At Audible, you will have an opportunity to make the best of your skillsets to both develop advanced scientific solutions and drive critical customer and business impact. You will play a key role to drive end-to-end solutions from understanding our business and business requirements, identifying opportunities from a large amount of historical data and engaging in research to solve the business problems. You'll seek to create value for both stakeholders and customers and inform findings in a clear, actionable way to managers and senior leaders. You will be at the heart of an agile and growing area at Audible. ABOUT THE TEAM Audible Data Scientists are members of a global interdisciplinary insights and research team with an integral role in the design and integration of models to automate decision making throughout the business in every country. We empower the machine learning and deep learning techniques in many areas of the business. We translate business goals into agile, insightful analytics and seek to create value for both stakeholders and customers and convey findings in a clear, actionable way to managers and senior leaders. As a Data Scientist, you will... - Design and execute geo-level randomized experiments to measure incremental impact - Apply statistical techniques to evaluate causal impact in quasi-experimental settings - Ensure experiments are statistically valid by evaluating sampling strategies, statistical power, and potential sources of bias - Develop models that estimate long-term effects from short-term experiments using machine learning - Estimate how changes in customer behavior persist and decay over time - Own and maintain the geo-testing codebase, including deployment and scalability - Implement machine learning models at scale with focus on performance optimization - Partner with stakeholders to ensure models align with real business dynamics - Engage deeply with business problems through curiosity-driven questioning and brainstorming - Translate experimental results into financial impact and investment recommendations - Analyze marginal and average revenue impacts relative to costs - Communicate complex quantitative ideas clearly to non-technical stakeholders - Demonstrate understanding of Audible's business model and customer experience ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Research Scientist, Modeling and Optimization (MOP)
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, WA, Bellevue

            
        


        
    
Do you enjoy solving challenging problems and driving innovations in research? Are you seeking for an environment with a group of motivated and talented scientists like yourself? Do you want to create scalable optimization models and apply machine learning techniques to guide real-world decisions? Do you want to play a key role in the future of Amazon transportation and operations? Come and join us at Amazon's Modeling and Optimization team (MOP). Key job responsibilities A Research Scientist in the Modeling and Optimization (MOP) team - provides analytical decision support to Amazon planning teams via applying advanced mathematical and statistical techniques. - collaborates effectively with Amazon internal business customers, and is their trusted partner - is proactive and autonomous in discovering and resolving business pain-points within a given scope - is able to identify a suitable level of sophistication in resolving the different business needs - is confident in leveraging existing solutions to new problems where appropriate and is independent in designing and implementing new solutions where needed - is aware of the limitations of their proposed solutions and is proactive in communicating them to the business, and advances the application of sciences towards Amazon business problems by bringing new methods, ideas, and practices to the team and scientific community. A day in the life - Your will be developing model-based optimization, simulation, and/or predictive tools to identify and evaluate opportunities to improve customer experience, network speed, cost, and efficiency of capital investment. - You will quantify the improvements resulting from the application of these tools and you will evaluate the trade-offs between potentially competing objectives. - You will develop good communication skills and ability to speak at a level appropriate for the audience, will collaborate effectively with fellow scientists, software development engineers, and product managers, and will deliver business value in a close partnership with many stakeholders from operations, finance, IT, and business leadership. About the team - At the Modeling and Optimization (MOP) team, we use mathematical optimization, algorithm design, statistics, and machine learning to improve decision-making capabilities across WW Operations and Amazon Logistics. - We focus on transportation topology, labor and resource planning for fulfillment facilities, routing science, visualization research, data science and development, and process optimization. - We create models to simulate, optimize, and control the fulfillment network with the objective of reducing cost while improving speed and reliability. - We support multiple business lanes, therefore maintain a comprehensive and objective view, coordinating solutions across organizational lines where possible.




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Applied Scientist, Demand Forecasting
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, WA, Bellevue

            
        


        
    
What does it take to build a foundation model that can forecast demand for hundreds of millions of products — including ones that have never been sold before? At Amazon, our Demand Forecasting team is tackling one of the most ambitious challenges in applied time series research: designing and building large-scale foundation models that generalize across an enormous and diverse catalog of products, geographies, and business contexts. This is not incremental modeling work. We are redefining what's possible in demand forecasting through novel architectures, training strategies, and data generation techniques. Our team operates at a scale that is unmatched in industry or academia. You'll design experiments across millions of products simultaneously, developing new model architectures and training methodologies that push the boundaries of what foundation models can learn from vast, heterogeneous time series data. You'll explore techniques in transfer learning, zero-shot forecasting, and synthetic data generation. The models you design here will ship to production and directly influence hundreds of millions of dollars in automated inventory decisions every week. Beyond operational impact, you'll publish your work at top-tier conferences and contribute to advancing the state of the art in time series foundation models for the broader scientific community. If you are a scientist who wants to work at the frontier of time series research, design novel solutions to problems no one else has solved at this scale, and see your research deployed to real-world impact — this is the team for you. Key job responsibilities 1. Design and implement novel deep learning architectures (e.g., Transformers, SSMs, or Graph Neural Networks) for time-series foundation models that generalize across hundreds of millions of products and diverse global contexts. 2. Drive the full development cycle - from whiteboarding new algorithmic approaches to overseeing production-scale deployments. 3. Collaborate with SDEs to build high-performance, distributed training and inference pipelines; translate complex scientific concepts into scalable, production-grade code in Python and Scala. 4. Leverage and develop agentic GenAI workflows to automate the end-to-end research cycle from synthesizing state-of-the-art literature and auto-generating experimental code to rapidly iterating on model architectures across millions of products. 5. Maintain a high bar for scientific excellence by publishing novel research in top-tier venues (e.g., NeurIPS, ICLR, KDD) and contributing to Amazon’s internal patent and science community. A day in the life No two days look the same, but most will involve a high-velocity blend of deep architectural work, distributed system design, and frontier scientific thinking at a scale you won’t find anywhere else. You might start the morning by designing a synthetic data pipeline to stress-test your foundation model. You’ll use generative techniques to simulate rare "black swan" supply chain events, ensuring your model remains robust where historical data is thin. You'll then lead a Scientific Design Review, walking senior leaders through your model’s architecture, defending your choice of loss functions with data-driven rigor. You’ll write high-performance code often paired with AI-coding assistants to handle the heavy lifting of boilerplate and unit testing. You’ll collaborate across a "Two-Pizza Team" of scientists and engineers, pushing the boundaries of research with a clear goal: contributing to work that will be published at top-tier venues (ICLR, NeurIPS) while simultaneously driving multi-million dollar automated decisions. The work is hard, the math is complex, and the tools are state-of-the-art. If you want to build the models that actually ship—this is where you do it. About the team The Demand Forecasting team sits at the heart of Amazon's supply chain, building the science that determines what products are available, when, and at what cost — for hundreds of millions of customers around the world. Our mission is to push the frontier of what's possible in large-scale time series forecasting, and to deploy that science where it creates real, measurable impact. We are a team of scientists who care deeply about both research rigor and real-world outcomes. We don't just publish — we ship. And we don't just ship — we measure, iterate, and raise the bar. Our work spans the full lifecycle: from foundational research and large-scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Member of Technical Staff - Applied Science , AGI Autonomy Lab
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, CA, San Francisco

            
        


        
    
Amazon has launched a new research lab in San Francisco to develop foundational capabilities for useful AI agents. We’re enabling practical AI to make our customers more productive, empowered, and fulfilled. Our work leverages large vision language models (VLMs) with reinforcement learning (RL) and world modeling to solve perception, reasoning, and planning to build useful enterprise agents. Our lab is a small, talent-dense team with the resources and scale of Amazon. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. We’re entering an exciting new era where agents can redefine what AI makes possible. Key job responsibilities You will contribute directly to AI agent development in an applied research role to improve the multi-model perception and visual-reasoning abilities of our agent. Daily responsibilities including model training, dataset design, and pre- and post-training optimization. You will be hired as a Member of Technical Staff.




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Data Scientist II, Amazon Stores Finance Science
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, WA, Seattle

            
        


        
    
WW Amazon Stores Finance Science (ASFS) works to leverage science and economics to drive improved financial results, foster data backed decisions, and embed science within Finance. ASFS is focused on developing products that empower controllership, improve business decisions and financial planning by understanding financial drivers, and innovate science capabilities for efficiency and scale. We are looking for a data scientist to lead high visibility initiatives for forecasting Amazon Stores' financials. You will develop new science-based forecasting methodologies and build scalable models to improve financial decision making and planning for senior leadership up to VP and SVP level. You will build new ML and statistical models from the ground up that aim to transform financial planning for Amazon Stores. We prize creative problem solvers with the ability to draw on an expansive methodological toolkit to transform financial decision-making with science. The ideal candidate combines data-science acumen with strong business judgment. You have versatile modeling skills and are comfortable owning and extracting insights from data. You are excited to learn from and alongside seasoned scientists, engineers, and business leaders. You are an excellent communicator and effectively translate technical findings into business action. Key job responsibilities Demonstrating thorough technical knowledge, effective exploratory data analysis, and model building using industry standard ML models Working with technical and non-technical stakeholders across every step of science project life cycle Collaborating with finance, product, data engineering, and software engineering teams to create production implementations for large-scale ML models Innovating by adapting new modeling techniques and procedures Presenting research results to our internal research community




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Senior Applied Scientist, People eXperience & Technology Central Science (PXTCS)
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, WA, Seattle

            
        


        
    
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve the employee and manager experience at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science! The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are seeking a senior Applied Scientist with expertise in more than one or more of the following areas: machine learning, natural language processing, computational linguistics, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling. In this role, you will lead and support research efforts within all aspects of the employee lifecycle: from candidate identification to recruiting, to onboarding and talent management, to leadership and development, to finally retention and brand advocacy upon exit. The ideal candidate should have strong problem-solving skills, excellent business acumen, the ability to work independently and collaboratively, and have an expertise in both science and engineering. The ideal candidate is not methods-driven, but driven by the research question at hand; in other words, they will select the appropriate method for the problem, rather than searching for questions to answer with a preferred method. The candidate will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders). About the team We are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Data Scientist, Digital Acceleration
            

        

        

            
                
                    

                
            

            

            

            

            
                
IN, TN, Chennai

            
        


        
    
Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazon's digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action? The Amazon Digital Acceleration Analytics team is looking for an analytical and technically skilled individual to join our team. In this role, you will invent, build and deploy state of the art machine-learning models and systems to enable and enhance the team's mission This role offers wide scope, autonomy, and ownership. You will work closely with software engineers & data engineers to put algorithms into practice. You should have strong business judgement, excellent written and verbal communication skills. The candidate should be willing to take on challenging initiatives and be capable of working both independently and with others as a team. Key job responsibilities We are looking for an experienced data scientist with strong foundations in mathematics, statistics & machine learning with exceptional communication and leadership skills, and a proven track record of delivery. In this role, You will Define a long-term science vision and roadmap for the team, driven fundamentally from our customers' needs, translating those directions into specific plans for engineering teams. Design and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. Drive end-to-end statistical analysis that have a high degree of ambiguity, scale, and complexity. Research and develop advanced Generative AI based solutions to solve diverse customer problems. About the team The MIDAS team operates within Amazon's Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Applied Scientist, AI Automation, Prime Video
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, WA, Seattle

            
        


        
    
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are forming a new organization within Prime Video to redefine our operational landscape through the power of artificial intelligence. As a Applied Scientist within this initiative, you will be a technical leader helping to design and build the intelligent systems that power our vision. You will tackle complex and ambiguous problems, designing and delivering scalable and resilient agentic AI and ML solutions from the ground up. You will not only write high-quality, maintainable software and models, but also mentor other scientists, influence our technical strategy, and drive engineering best practices across the team. Your work will directly contribute to making Prime Video's operations more efficient and will set the technical foundation for years to come. We're seeking candidates with strong experience in computer vision and generative AI technologies. In this role, you'll apply cutting-edge techniques in image and video understanding, visual content generation, and multimodal AI systems to transform how Prime Video operates at scale. Key job responsibilities • Lead the design and architecture of highly scalable, available, and resilient services for our AI automation platform. • Write high-quality, maintainable, and robust code to solve complex business problems, building flexible systems without over-engineering. • Act as a technical leader and mentor for other engineers on the team, assisting with career growth and encouraging excellence. • Work through ambiguous requirements, cut through complexity, and translate business needs into scalable technical solutions. • Take ownership of the full software development lifecycle, including design, testing, deployment, and operations. • Work closely with product managers, scientists, and other engineers to build and launch new features and systems. About the team This role offers a unique opportunity to shape the future of one of Amazon's most exciting businesses through the application of AI technologies. If you're passionate about leveraging AI to drive real-world impact at massive scale, we want to hear from you.




        

        

        

        
    


    



                    

                
                    

                        

    

    

        
            

                Applied Scientist, Frontier AI & Robotics (FAR)
            

        

        

            
                
                    

                
            

            

            

            

            
                
US, CA, San Francisco

            
        


        
    
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As an Applied Scientist, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.




        

        

        

        
    


    



                    

                
            

        

    

    

        See more jobs
    






        




    

        
            
        
        

            

                
                    

                        

                    

                
            

            
                

                    

                        

  

      
          

              
                  
    
        View from space of a connected network around planet Earth representing the Internet of Things.
    

              
          

      

      

        

          
              
Get more from Amazon Science

          

          
    
Subscribe to our monthly newsletter



        

        
          
        
      

  



                    

                

            
        

    


    

        

        
            
Amazon.com | Conditions of Use | Privacy | © 1996-2026 Amazon.com, Inc. or its affiliates

        
        
            

    
Social