The science behind Astro's graceful, responsive motion

Predictive planning, uncertainty modeling, uniquely constrained trajectory optimization, and multiscale planning help customers trust Astro.

With Astro, we are building something that was a distant dream just a few years ago: a mobile robot that can move with grace and confidence, can interact with human users, and is available at a consumer-friendly price.

Related content
“Body language” and an awareness of social norms help Amazon’s new household robot integrate gracefully into the home.

Since Astro is a consumer robot, its sensor field of view and onboard computational capabilities are highly constrained. They are orders of magnitude less powerful than those of some vehicles used in industrial applications and academic research. Delivering state-of-the-art quality of motion under such constraints is challenging and necessitates innovation in the underlying science and technology. But that is what makes the problem exciting to researchers and to the broader robotics community.

This blog post describes the innovations in algorithm and software design that enable Astro to move gracefully in the real world. We talk about how predictive planning, handling uncertainties, and robust and fast optimization are at the heart of Astro’s motion planning. We also give an overview of Astro’s planning system and how each layer handles specific spatial and temporal aspects of the motion-planning problem.

Computation, latency, and smoothness of motion

For motion planning, one of the fundamental consequences of having limited computational capacity is a large sensing-to-actuation latency: it can take substantial time to process sensor data and to plan robot movements, which in turn has significant implications for smoothness of motion.

Related content
Deep learning to produce invariant representations, estimations of sensor reliability, and efficient map representations all contribute to Astro’s superior spatial intelligence.

As an example, let’s assume it takes 500 milliseconds to process raw sensor data, to detect and track obstacles, and to plan the robot’s movements. That means that a robot moving at one meter per second would have moved 50 centimeters before the sensor data could have any influence on its movement! This can have a huge impact on not only safety but also smoothness of motion, as delayed corrections usually need to be larger, causing jerky movements.

Astro tries to explicitly compensate for this with predictive planning.

Predictive planning

Astro not only tries to predict movements of external objects (e.g., people) but also estimates where it will be and what the world will look like at the end of the current planning cycle, fully accounting for the latencies in the sensing, mapping, and planning pipeline. Astro’s plans are based on fast-forwarded states: they’re not based just on the latest sensor data but on what Astro believes the world will look like in the near future, when the plan will actually take effect.

If the predictions are reasonably good, this kind of predictive planning can critically reduce the impact of unavoidable latencies, and Astro’s observed smoothness of motion depends in large part on our predictive planning framework. However, that framework requires careful handling of uncertainties, as no prediction is ever going to be perfect.

Handling uncertainties

For motion planning, uncertainty can directly translate to risk of collision. Many existing academic methods either treat risk as a special type of constraint — e.g., allowing all motion if the risk is below some preset threshold (so-called chance constraints) — or rely on heuristic risk-reward tradeoffs (typically via a constant weighted sum of costs). These approaches tend to work well in cases where risk is low but do not generalize well to more challenging real-world scenarios.

Related content
Measuring the displacement between location estimates derived from different camera views can help enforce the local consistency vital to navigation.

Our approach relies on a unique formulation where the robot’s motivation to move toward the goal gets weighed dynamically via the perceived level of uncertainty. The objective function is constructed so that Astro evaluates uncertainty-adjusted progress for each candidate motion, which allows it to focus on getting to the goal when risk is low but focus on evasion when risk is high.

It is worth noting that in our formulation, there is no discrete transition between high-risk and low-risk modes, as the transition is handled via a unified, continuous cost formulation. Such absence of abrupt transitions is important for smoothness of motion.

When you see Astro automatically modulating its speed smoothly as it gets near obstacles and/or avoids an oncoming pedestrian, our probabilistic cost formulation is at play.

Trajectory optimization

To plan a trajectory (a time series of positions, velocities, and accelerations), Astro considers multiple candidate trajectories and chooses the best one in each planning cycle. Our formulation allows Astro to plan 10 times a second, evaluating a few hundred trajectory candidates in each instance. Each time, Astro finds the trajectory that will result in the optimal behavior considering safety, smoothness of motion, and progress toward the goal.

Theoretically, there are always infinitely many trajectories for a planner to choose from, so exhaustively searching for the best trajectory would take forever.

But not all trajectory candidates are useful or desirable. In fact, we observe that most trajectories are jerky, and some of them are not even realizable on the physical device. Restricting the candidates to smooth and realizable trajectories can drastically reduce the size of the search space without reducing the robot’s ability to move.

robot_trajectory_distribution.png
For efficient search, Astro’s trajectory optimization relies on a compact space of smooth and realizable trajectories. Astro is depicted as a magenta rectangle in the middle, and the colored curves are 600 trajectories randomly sampled from the trajectory space.

Unlike other approaches, which reduce the number of choices to a discrete set (e.g., a state lattice), our formulation is continuous; it thus improves smoothness as well as safety, via the fine-grained control it enables. Our special trajectory parameterization also guarantees that all of the trajectories in the space are physically realizable.

The search space still retains enough diversity of trajectories to include quick stops and hard turns; these may become necessary when a dynamic obstacle suddenly enters Astro’s field of view, when there is a small or difficult-to-see obstacle that is detected too late, or simply when Astro is asked to switch to a new task as quickly as possible.

Related content
A new opt-in feature for Echo Show and Astro provides more-personalized content and experiences for customers who choose to enroll.

We also pay necessary attention to detail in the implementation, such as multistage optimization and warm-starting to avoid local minima and enable faster convergence. All of these contribute to the smoothness of motion.

Whole-body trajectory planning

Astro’s planning system controls more than just two wheels on a robot body. It also moves Astro’s screen, which is used not only for visualizing content but also for communicating motion intent (looking where to go) and for active perception (looking at the person Astro is following using the camera on the display). The communication of intent via body language and active perception help enable more robust human-robot interactions.

We won’t go into much into the detail here, but we would like to mention that the predictive planning framework also helps here. Knowing what the robot should do with its body, and also knowing the predicted location of target objects in the near future, can often make the planning of the screen movements trivial.

planning-a-trajectory-screen-borders.png
A snapshot of Astro’s continuous trajectory planning. Colored curves represent trajectory candidates within the next three seconds. (For clarity, only 10% of all trajectories evaluated are shown here. Green is better; red is worse.) Blue arrows indicate longer-range path guidance. Astro (magenta box) is turning its screen (smaller box in front) to the left, indicating that it is planning to turn slightly to the left.

The planning system: temporal and spatial decomposition

So far, we’ve discussed how Astro plans its local trajectories. In this section, we give an overview of Astro’s planning system (of which the trajectory planner is one layer) and describe how the whole system works cooperatively. In our design, we decompose the motion-planning problem into three planning layers with varying degree of spatial and temporal coverage. The entire system is built to work together to generate the smooth and graceful motion we desire.

planning layers.png
Astro’s planning system is composed of the following three layers: the global path-planning layer, the local trajectory-planning layer, and the reactive control layer. From global to reactive, each layer has progressively less spatial coverage (and hence less data per input) but runs at a higher frequency.

Global path planning

The global path planner is responsible for finding a path from the current robot position to a goal specified by the user, considering historically observed navigability information (e.g., door opened/closed). This is the only layer in the system that has access to the entire global map, and it is expected to have a larger latency due to the amount of data it processes.

Related content
The professor of collective intelligence and robotics at the University of Cambridge earned a 2019 Amazon Research Award for “Learning Explicit Communication for Multi-Robot Path Planning”.

Because of that latency, the global planner is run on demand. Once it finds a path in the current global map, we rely on downstream layers to make Astro move smoothly along the path and to more quickly respond to higher-frequency changes in the environment.

Local trajectory planning

The local trajectory planner is responsible for finding a safe and smooth trajectory that will make good progress along the path provided by the global path planner. Unlike global planning, which has to process the entire map, it considers a fixed and limited amount of data (a six-by-six-meter local map). This allows us to guarantee that it will maintain a constant replanning rate of 10 Hz, with a three-second planning horizon.

This is a layer where we can really address smoothness of motion, as it considers in detail the exact shapes and dynamics of the robot and various semantic entities in the world.

Related content
Company is testing a new class of robots that use artificial intelligence and computer vision to move freely throughout facilities.

As can be seen above, Astro’s planned trajectories do not coincide exactly with a given global path. This is because we intentionally treat the global path as a guidance: the local trajectory planner has a lot of flexibility in determining how to progress along the path, considering the dynamics of the robot and the world. This flexibility not only makes the job easier for the local trajectory planner but also reduces the burden on the global planner, which can focus on finding an approximate guidance with loose guarantees rather than an explicit and smooth path.

Reactive control

Finally, we have a reactive control layer. It deals with a much smaller map (a two-by-two-meter local map), which is updated with much lower latency. At this layer, we perform our final check on the planned trajectory, to guard against surprises that the local trajectory planner cannot address without incurring latency.

Related content
Navigation, perception, simulation — three key components to giving Amazon Scout true independence.

This layer is responsible for handling noise and small disturbances at the state estimation level and also for quickly slowing down or sometimes stopping the robot in response to more immediate sensor readings. Not only does this low-latency slowdown reduce Astro’s time of reaction to surprise obstacles, but it also gives the local mapper and trajectory planner extra time to map obstacles and plan alternate trajectories.

The path forward

With Astro, we believe we have made considerable progress in defining a planning system that is lightweight enough to fit within the budget of a consumer robot but powerful enough to handle a wide variety of dynamic, ever-changing home environments. The intelligent, graceful, and responsive motion delivered by our motion-planning algorithms is essential for customers to trust a home robot like Astro.

But we are most certainly not done. We are actively working on improving our mathematical formulations and engineering implementations, as well as developing learning-based approaches that have shown great promise in recent academic research. As Astro navigates more home environments, we expect to learn much more about the real-world problems that we need to solve to make our planning system more robust and, ultimately, more useful to our customers.

Research areas

Related content

GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for ML Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities As an ML Data Scientist, you will * Collaborate with ML scientist and architects to Research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges * Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production * Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder * Provide customer and market feedback to Product and Engineering teams to help define product direction About the team The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
GB, Cambridge
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As an Applied Scientist on our team you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in multi-modal AGI models, with a focus on speech generation. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to build Machine Learning models for their application in speech generation. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Our team's mission is to improve Shopping experience for customers interacting with Amazon devices via voice. We work with Alexa and multiple other teams to research and develop advanced state-of-the-art speech technologies. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. Key job responsibilities We are looking for a passionate, talented, and inventive Research Scientist with a background in Machine Learning to help build industry-leading Speech and Language technology. As a Research Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for Speech and Language applications. * Participate in research activities including the application and evaluation of Speech and Language techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business.
US, WA, Seattle
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. In 2019, Amazon co-founded The Climate Pledge and made a commitment to achieve net-zero carbon by 2040 —10 years ahead of the Paris Agreement. We invited others to join us and there are now more than 300 businesses and organizations across 51 industries and 29 countries that have signed the Pledge, which means we are collectively coming at the climate crisis from nearly every sector and nearly every angle. As part of our efforts to decarbonize our business, we became the world’s largest corporate purchaser of renewable energy in 2020, and last year, we reached 85% renewable energy across our business, and are on a path to power our operations with 100% renewable energy by 2025. We recently announced that AWS will be water positive by 2030, returning more water to communities than it uses in its direct operations. The company also announced its 2021 global water use efficiency (WUE) metric of 0.25 liters of water per kilowatt-hour, demonstrating AWS’s leadership in water efficiency among cloud providers. To learn more about AWS’s water+ commitment visit: Water Stewardship. Come join the team that is building the tools and innovative technology to manage our growing portfolio of renewable energy investments, including solar, on-shore and off-shore wind farms. Key job responsibilities As an data scientist, you will employ machine learning and analytics to create scalable solutions for problems in sustainable energy space. You will dissect large historical business data sets to enhance and streamline essential processes. You will partner with data and software teams to create models for predictive insights and establish automated methods for large data analysis. A day in the life To learn more, you can visit: amazon sustainability in the cloud About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
US, VA, Arlington
As cloud technologies continue to transform businesses, skilled individuals are in high demand. At AWS Training and Certification (T&C), we are passionate about revolutionizing the way people advance their cloud skills and careers. We equip diverse builders of today and tomorrow with the knowledge they need to leverage the power of the AWS Cloud. Join our dynamic, fast-growing team and help us empower our customers to build cloud skills. The AWS Certification team is seeking a Psychometrician with experience working with criterion-referenced assessment programs to support a large global AWS Certification program. In this role, you will support all psychometric aspects of exam development and operation, including job analyses, standard setting, automated test assembly, item and test analyses, optimal item bank design, quality assurance, and project planning. You will work closely with a team of psychometricians, subject matter experts, certification exam program managers, publishing, delivery, security, product management, and translation/localization teams to support ongoing analyses of exam data. To be successful in this position, you must be highly motivated, creative, detail oriented, and a self-starter who is able to think big, execute, ensure high quality, yet stay focused on the details. Key job responsibilities - Conduct Job Task Analysis (JTA) workshops and post-JTA survey analyses to define the blueprint and test specifications for new certifications or updates to existing certifications - Conduct standard setting studies to set the passing score for an exam - Run item analysis to evaluate quality and performance of exam items - Use automated test assembly procedures to assemble forms or item pools - Work with content development to optimize the health of item banks - Support the development of a cloud-based analytics and reporting system - Interpret and clearly communicate the results of analyses to stakeholders through written and oral reports - Follow the accreditation standards set by ISO/IEC:2012 17024 and the National Council for Certifying Agencies (NCCA) as they relate to valid psychometric practices - Contribute to the development and execution of the strategic goals regarding the AWS certification program - Consult with leadership, internal staff, external consultants, and industry leaders regarding advancement of current offerings
IL, Haifa
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. We are looking for an Applied Scientist to embark on our journey to build a Prime Video Sports tech team in Israel from ground up. Our team will focus on developing products to allow for personalizing the customers’ experience and providing them real-time insights and revolutionary experiences using Computer Vision (CV) and Machine Learning (ML). You will get a chance to work on greenfield, cutting-edge and large-scale engineering and science projects, and a rare opportunity to be one of the founders of the Israel Prime Video Sports tech team in Israel. Key job responsibilities We are looking for an Applied Scientist with domain expertise in Computer Vision or Recommendation Systems to lead development of new algorithms and E2E solutions. You will be part of a team of applied scientists and software development engineers responsible for research, design, development and deployment of algorithms into production pipelines. As a technologist, you will also drive publications of original work in top-tier conferences in Computer Vision and Machine Learning. You will be expected to deal with ambiguity! We're looking for someone with outstanding analytical abilities and someone comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go. About the team In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.
US, WA, Seattle
The PeopleInsight (PI) org focuses on improving employee experience at Amazon, driving productivity and retention, and resulting in a motivated workforce of over 1.5 million associates and corporate employees. These are the questions we ask — Are we facilitating the right conversations to build an engaged workforce? What trends are we seeing in our employee data and what should managers do about it? How do we solve customer problems in the most efficient way possible? If these challenges sound interesting to you, you want to be a part of building ‘first of their kind’ products, and you are passionate about putting employee experience first, consider the PeopleInsight team. PI helps Amazon drive improvements in employee talent outcomes (e.g., job satisfaction and retention), and strive to be Earth’s Best Employer through scalable technology. PI is looking for a customer-obsessed Data Scientist for Employee Engagement Services, a suite of internal employee engagement and recognition products supporting Amazonians WW, with a strong track record of delivering results and proven research experience. This role will own and execute strategic cross-functional employee engagement experiments, analysis and research initiatives across Operations and Corporate audiences for high CSAT products. The Data Scientist must love extracting, cleaning and transforming high volume of data into actionable business information and be able to drive actionable insights. The data scientist will partner with Product, UX and Dev teams to own end-to-end business problems and metrics with a direct impact on employee experience. Success in this role will include influencing within your team and mentoring peers. The problems you will consider will be difficult to solve and often require a range of data science methodologies combined with subject matter expertise. You will need to be capable of gathering and using complex data set across domains. You will deliver artifacts on medium size projects, define the methodology, and own the analysis. Your findings will affect important business decisions. Solut Key job responsibilities • Implement statistical methods to solve specific business problems utilizing code (Python, R, Scala, etc.). • Development of user classification models and other predictive models to enable a personalized experience for a user. • Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. • Collaborate with product management, software developers, data engineering, and business leaders to define product requirements, provide analytical support, and communicate feedback; develop, test and deploy a wide range of statistical, econometric, and machine learning models. • Build customer-facing reporting tools to provide insights and metrics which track model performance and explain variance. • Communicate verbally and in writing to business customers with various levels of technical knowledge, educating them about our solutions, as well as sharing insights and recommendations. • Earn the trust of your customers by continuing to constantly obsess over their needs and helping them solve their problems by leveraging technology About the team The PeopleInsight team is a collaborative group of Business Intelligence Engineers, Data Scientists, Data Engineers, Research Scientists, Product Managers, Software Development Engineers, Designers and Researchers that studies a workforce numbering in the hundreds of thousands. Our work is dedicated to empowering leaders and enabling action through data and science to improve the workplace experience of associates and ensure Amazon is Earth's Best Employer.
LU, Luxembourg
Have you ever wished to build high standard Operations Research and Machine Learning algorithms to optimize one of the most complex logistics network? Have you ever ordered a product on Amazon websites and wondered how it got delivered to you so fast, and what kinds of algorithms & processes are running behind the scenes to power the whole operation? If so, this role is for you. The team: Global transportation services, Research and applied science - Operations is at the heart of the Amazon customer experience. Each action we undertake is on behalf of our customers, as surpassing their expectations is our passion. We improve customer experience through continuously optimizing the complex movements of goods from vendors to customers throughout Europe. - Global transportation analytical teams are transversal centers of expertise, composed of engineers, analysts, scientists, technical program managers and developers. We are focused on Amazon most complex problems, processes and decisions. We work with fulfillment centers, transportation, software developers, finance and retail teams across the world, to improve our logistic infrastructure and algorithms. - GTS RAS is one of those Global transportation scientific team. We are obsessed by delivering state of the art OR and ML tools to support the rethinking of our advanced end-to-end supply chain. Our overall mission is simple: we want to implement the best logistics network, so Amazon can be the place where our customers can be delivered the next-day. The role: Applied scientist, speed and long term network design The person in this role will have end-to-end ownership on augmenting RAS Operation Research and Machine Learning modeling tools. They will help understand where are the constraints in our transportation network, and how we can remove them to make faster deliveries at a lower cost. Concretely, you will be responsible for designing and implementing state-of-the-art algorithmic in transportation planning and network design, to expand the scope of our Operations Research and Machine Learning tools, to reflect the constantly evolving constraints in our network. You will enable the creation of a product that drives ever-greater automation, scalability and optimization of every aspect of transportation, planning the best network and modeling the constraints that prevent us from offering more speed to our customer, to maximize the utilization of the associated resources. The impact of your work will be in the Amazon EU global network. The product you will build will span across multiple organizations that play a role in Amazon’s operations and transportation and the shopping experience we deliver to customer. Those stakeholders include fulfilment operations and transportation teams; scientists and developers, and product managers. You will understand those teams constraints, to include them in your product; you will discuss with technical teams across the organization to understand the existing tools and assess the opportunity to integrate them in your product. You will also be challenged to think several steps ahead so that the solutions you are building today will scale well with future growth and objective (e.g.: sustainability). You will engage with fellow scientists across the globe, to discuss the solutions they have implemented and share your peculiar expertise with them. This is a critical role and will require an aptitude for independent initiative and the ability to drive innovation in transportation planning and network design. Successful candidates should be able to design and implement high quality algorithm solutions, using state-of-the art Operations Research and Machine Learning techniques. You will have the opportunity to thrive in a highly collaborative, creative, analytical, and fast-paced environment oriented around building the world’s most flexible and effective transportation planning and network design management technology. Key job responsibilities - Engage with stakeholders to understand what prevents them to build a better transportation network for Amazon - Review literature to identify similar problems, or new solving techniques - Build the mathematical model representing your problem - Implement light version of the model, to gather early feed-back from your stakeholders and fellow scientists - Implement the final product, leveraging the highest development standards - Share your work in internal and external conferences - Train on the newest techniques available in your field, to ensure the team stays at the highest bar About the team GTS Research and Applied Science is a team of 15 scientists and engineers whom mission is to build the best decision support tools for strategic decisions. We model and optimize Amazon end-to-end operations. The team is composed of enthusiastic members, that love to discuss any scientific problem, foster new ideas and think out of the box. We are eager to support each others and share our unique knowledge to our colleagues.
US, WA, Seattle
This is a unique opportunity for a postdoc to work on research projects that investigate state of the art NLP, IR, and LLM approaches for understanding retail products and their pricing. This will include working with billion-scale datasets and investigating how the world knowledge captured by LLMs reflects real world prices, and investigating more advanced prompting and reasoning techniques to construct large knowledge graphs that are specialized for various pricing use cases such as probabilistic price estimation, as well as error detection and correction. Key job responsibilities In this role you will: • Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. • Publish your innovation in top-tier academic venues and hone your presentation skills. • Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise. About the team The retail pricing science team is a centralized diverse team of STEM scientists that develop statistical, ML, RL, optimization and economic models that drive pricing for products sold by Amazon worldwide, as well as monitoring of prices and experimentations in pricing. The team has a dual focus on competitiveness and long term financial optimality.
US, CA, East Palo Alto
AWS Analytics is looking for a passionate, inventive Applied Scientist with a strong background in either machine learning, programming languages or databases to help create industry-leading analytics experiences powered by generative AI, machine learning, and program analysis. AWS provides a comprehensive set of analytics services for all data analytics needs and enables organizations of all sizes and industries to reinvent their business with data. From storage and management, data governance, actions, and experiences, AWS offers purpose-built services that provide the best price-performance, scalability, and lowest cost. We are a team dedicated to delivering transformative, science-driven analytics experiences for Amazon customers and having fun doing so. Our leadership team fosters an inclusive team culture and encourages work-life balance to bring out the best in each team member. Collaboration and mentorship are key tenets of our fabric. We are a growing team dedicated to supporting new members achieve their aspirations. Key job responsibilities As part of the AWS Analytics science team you will have the opportunity to apply your skills in machine learning, program analysis, and databases to impact some of the largest analytics services in the industry and their customers. You will innovate by designing and building agent-based solutions orchestrating foundation models, machine learning models, and program analyses to simplify AWS customers’ analytics journey and optimize their cost-performance profile. You will collaborate with a talented team of applied science peers to drive scientific impact and with engineering, product, and business leaders to launch your work in production at Amazon scale. A day in the life A mix of the following activities: talking to product leaders and customers to define science features; researching the state of the art and creating science plans to build them; building and rigorously benchmarking the science implementations of such features; partnering with engineering teams to onboard science work and launch it in production; preparing, publishing, and presenting scientific work at top-tier science venues and evangelizing it within the company; upgrading your science knowledge by participating in reading groups and science presentations by internal or external scientists; mentoring applied science interns and science peers in all of the above functions. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.