A screen grab from an NFL video shows Packers quarterback Aaron Rodgers preparing to pass the ball
In January, the National Football League announced its new QB passing score, which addressed the inconsistency across plays, games, weeks, and seasons found in previous scores. A method based on spliced binned-Pareto distributions, developed by Amazon researchers, led to the improved passing metric.

The science behind NFL Next Gen Stats’ new passing metric

Spliced binned-Pareto distributions are flexible enough to handle symmetric, asymmetric, and multimodal distributions, offering a more consistent metric.

When football fans evaluate a player’s performance, they measure the player’s execution of specific plays against an innate sense of the player’s potential. Trying to encode such judgments into machine learning models, however, has proved non-trivial.

Fans and commentators have criticized existing quarterback (QB) passing stats, such as Madden QB, the NFL passer rating, ESPN’s total quarterback rating (QBR), and the Pro Football Focus (PFF) grade, for being calibrated to obsolete data, being unrelated to winning, or scoring players anomalously — as when Kyler Murray received the low Madden QB21 rating of 77 despite being the 2019 Offensive Rookie of the Year.

Related content
Principal data scientist Elena Ehrlich uses her skills to help a wide variety of customers — including the National Football League.

On January 13, 2022, just before Super Bowl LVI, the NFL announced its new QB passing score, which seeks to improve on its predecessors’ limitations and to isolate a QB’s contributions from those of the team in a completely data-driven way.

The play level

A root problem with existing ratings is their inconsistency across plays, games, weeks, and seasons. We sought a metric that could account for play-specific dynamics and scale to different granularities with consistency.

We wanted to measure the QB’s decision making and pass execution given the game clock and the pressure he was under. For those conditions, we have directly measurable quantities, such as the defense’s movements. But how do we measure how “well” the QB performed? This is a point we address in the next section (“The model architecture”), but for now, we take yards gained as a measurable outcome. (This assumption will prove useful downstream.)

nflendzonesideline.png
An (x, y)-coordinate representation of the football field.

Since we said we wanted to take a data-driven approach, let’s look at exactly what the data is.

On each play, we receive updates every 100 milliseconds from radio frequency ID chips in the players’ shoulder pads, giving us all 22 players’ position in the (x, y)-coordinates of the field, along with their speed, acceleration, running direction, and body orientation, as shown in the image above.

This time series is of variable length, starting with the snap and ending when the QB releases the ball. For example, a QB throwing four seconds after the snap yields a time series of 40 timesteps, whereas a pass that takes just over two seconds yields a time series of 25 timesteps.

Related content
In its collaboration with the NFL, AWS contributes cloud computing technology, machine learning services, business intelligence services — and, sometimes, the expertise of its scientists.

The figure below shows how the time series is represented. Each row corresponds to a single timestep and contains eight features (x-position, y-position, x-speed, y-speed, x-acceleration, y-acceleration, direction, and orientation) for each of 22 players, for a matrix of 176 columns and 40 rows. Features such as the number of defenders within a two-yard radius of the target receiver receive additional columns, but we eschew them here to focus on modeling technique.

nflplaytimeseriesmatrix.png
Matrix representation of the time series of a single play.

The collection of passing plays from the 2018-2020 seasons provided us with around 34,000 completions, 15,000 incompletes, and 1,200 interceptions, for more than 50,000 plays total. Feature preprocessing is a memory-intensive job, requiring two hours runtime on a ml.m5.m24xlarge instance. Modeling so large a number of time series, however, is a high-compute job.

For the model described in the upcoming section, the one-gpu p3.8xlarge instance incurred an eight-hour training time. While the NFL can afford two-hour preprocessing and eight-hour model fittings before the season commences, in live televised games, the inference returning a QB’s score for his play needs to be in real-time, like the 0.001 second per play of the following model.

The model architecture

To learn the temporal complexities within plays’ time series, we opted for a temporal convolutional network (TCN), a convolutional network adapted to handle inputs of different lengths and factor in long-range relationships between sequential inputs.

Since a play also has static attributes — such as down, score, and games remaining in the season — that influence players’ decisions and performance, we concatenate these with the TCN state and pass both to a multilayer perceptron to produce the final output, a probabilistic prediction of yards gained. To that, we compare the play’s actual yards gained.

nflplayertimeseriestcn.png
In our model, players’ time series are encoded by a temporal convolutional network (TCN), concatenated with a play’s static features, and fed to a multilayer perceptron.

Now, the network output is worth careful consideration. Naively, one might want to output a point prediction of the yards gained and train the network with an error loss function. But this fails to achieve the desired goal of measuring the outcome of a play relative to its potential.

An extra two yards gained under easier circumstances is not the same as two yards gained in more difficult circumstances, yet both would have a mean absolute error (MAE) of two yards. Instead, we opted for a distributional prediction, where the network’s outputs are parameters that specify a probability distribution.

We thought about which probability distribution function (PDF) would be most suitable. For certain plays, the PDF of yards gained would need to be asymmetrical: e.g., in a completed pass, if the QB throws to a receiver already running toward the end zone, positive yards gained are more likely than negative yards. Whereas for other plays, the PDF of yards gained would need to capture symmetry: on an interception, for example, the “negative” yards gained by the defender would balance against the possible positive yards gained by a completion.

There are even those plays for which the PDF would be bimodal: if the QB passes to a receiver with only one defender closing in, then the likelihood of yards gained lies either in the one- to two-yards range (if the receiver is tackled) or in the high-yardage range (if the receiver eludes the tackle), but not in-between. Other multi-model plays include when the QB may have to scramble for yards, like in the second play in this video.

yardsgainedpassescompletedgraphic.png
Yards gained on intercepted versus completed passes.

So we needed a distribution whose parameterization is flexible enough to accommodate multimodality, different symmetries, and light or heavy tails and whose locations and scale can vary with the clock time, current score, and other factors. We can’t meet these requirements with distributions like Gaussian or gamma, but we can meet them with the spliced binned-Pareto distribution.

The spliced binned-Pareto distribution

The spliced binned-Pareto (SBP) distribution arises from a classic result in extreme-value theory (EVT), which states that the distribution of extreme values (i.e., the tail) is almost independent of the base distribution of the data and, as shown below, can be estimated from the datapoints above the assumed upper bound (t) of the base distribution.

The second theorem of EVT states that any such distribution tail can be well-approximated by a generalized Pareto distribution (GPD) that has only two parameters, shape (x) and scale (b), and closed-form quantiles. The figure below shows the PDF of a GPD for x < 0, yielding a finite tail; x = 0, yielding an exponential tail; and x > 0, yielding a heavier-than-exponential tail.

valuesofdistribution.png
At left is a visualization of the observation that extreme values of a distribution (i.e., the tail) are almost independent of the base distribution and can be estimated from the datapoints above the assumed upper bound (t) of the base distribution. At right are probability distribution functions for generalized Pareto distributions with three different shapes.

Since we need multimodality and asymmetry for the base distribution, we modeled the base of the predictive distribution with a discrete binned distribution; as shown below, we discretize the real axis between two points into bins and predict the probability of the observation falling in each of these bins.

This yields a distribution robust to extreme values at training time because it is now a classification problem. The log-likelihood is not affected by the distance between the predicted mean and the observed point, as would be the case when using a Gaussian, Student’s t, or other parametric distribution. Moreover, the bins’ probability heights are independent of one another, so they can capture asymmetries or multiple modes in the distribution.

From the binned distribution, we delimit the lower tail by the fifth quantile and replace it with a weighted GPD. Analogously, we delimit the upper tail by the 95th quantile and replace it with another weighted GPD, to yield the SBP shown below.

binned and spliced binned graphic.png
At left is a binned distribution; at right is a spliced binned distribution, whose topmost and bottommost quantiles have been replaced with weighted generalized Pareto distributions.

The figure on the left above shows that the base distribution is indeed robust: the event represented by the extreme red dot will not bias the learned mean of the distribution but simply inflate the probability associated with the far-right bin.

However, this still leaves two problems: (i) although the red-dot event was observed to occur, the binned distribution would give it zero probability; conversely, (ii) the distribution would predict with certainty that extreme (i.e., great) plays do not occur. Because extreme yardage from deep-pass touchdowns, breakaway interceptions, etc., is rare, it is the adrenaline of the sport and exactly what we are most interested in describing probabilistically. The SBP figure above on the right graphically illustrates how the GPD tails can quantify how much less likely — i.e., harder — each incremental yard is.

The binned distribution and the GPDs are parameterized by the neural network we described above, which takes as input play matrices and outputs parameters: each of the bin probabilities, as well as x and b for each of the GPDs, which can be used to predict the probability-of-yards-gained value.

Establishing a gradient-based learning of heavy-tailed distributions has been a challenge in the ML community. Carreau and Bengio’s Hybrid Pareto model stitched GPD tails onto parametric distributions, but since the likelihood isn’t differentiable with respect to the threshold t, their model is supplemented with simulation and numerical approximations, foregoing time-varying applications. Other previous methods such as SPOT, DSPOT, and NN-SPOT, forego modeling the base and capture only the tails outside a fixed distance from the mean, which precludes higher-order non-stationarity and asymmetric tails.

While prior methods use a fixed threshold t to delimit tails, by modeling the base distribution, we obtain a time-varying threshold. Furthermore, training a single neural network to maximize the log-probability of the observed time step under the binned and GPD distributions yields a prediction that accounts for temporal variation in all moments of the distribution — the mean and variance as well as tail heaviness and scale, including asymmetric tails. The capabilities of different approaches are tabled below.

capabilitiesofdifferentapproaches.png
Capabilities of different approaches.

While we need a distributional prediction to grade a QB’s performance — to compare our model’s accuracy to other models’ — we need to use point predictions of yards gained. The table below compares the MAE of our method’s predictive median against that of a neural network with Gaussian output and against the point prediction of XGBoost, a decision-tree-based model.

meanaverageerror.png
Mean average error on yards gained for roughly 5,000 plays.

We have released Pytorch code for the spliced binned-Pareto model, along with a demo notebook.

The NGS passing score

Our model’s predictive PDF quantifies how likely each yardage gain is, for a league-average QB, given a specific play’s circumstances. Therefore, evaluating the actual yards gained in the cumulative distribution function (CDF) of that play’s SBP distribution yields a ranking between 0 and 1 of that QB’s performance relative to peer QBs.

This CDF ranking, under some further standardizations, becomes the QB passing score at the play level.

Aggregating scores over multiple plays yields game-, season-, or other split-level QB passing scores. For example, based on all targeted pass attempts in the ’21 season, Kyler Murray has a score of 87, ranking him ninth out of playoff QBs.

Under pressure, Murray's score jumps to 89; zooming in to passes between 2.5 and 4 seconds (in 2020 and 2021), Murray now scores a 99 in a five-way tie for the highest possible score. Other splits can also be contextualized with the NGS passing score, like deep passes, for example.

Finally, the tables below show that the NGS passing score correlates better with win percentages and playoff percentages than preceding passing metrics.

ngspassingscorespassingmetricsandwins.png
At left is the correlation of passing score with winning percentages and playoff percentages. At right is the comparison of passing score and other metrics.

Acknowledgments: Brad Gross

Research areas

Related content

GB, MLN, Edinburgh
We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities for every candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problems. Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
IN, TS, Hyderabad
Welcome to the Worldwide Returns & ReCommerce team (WWR&R) at Amazon.com. WWR&R is an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, our company, and the environment. Our goal is to achieve the three zeroes: zero cost of returns, zero waste, and zero defects. We do this by developing products and driving truly innovative operational excellence to help customers keep what they buy, recover returned and damaged product value, keep thousands of tons of waste from landfills, and create the best customer returns experience in the world. We have an eye to the future – we create long-term value at Amazon by focusing not just on the bottom line, but on the planet. We are building the most sustainable re-use channel we can by driving multiple aspects of the Circular Economy for Amazon – Returns & ReCommerce. Amazon WWR&R is comprised of business, product, operational, program, software engineering and data teams that manage the life of a returned or damaged product from a customer to the warehouse and on to its next best use. Our work is broad and deep: we train machine learning models to automate routing and find signals to optimize re-use; we invent new channels to give products a second life; we develop highly respected product support to help customers love what they buy; we pilot smarter product evaluations; we work from the customer backward to find ways to make the return experience remarkably delightful and easy; and we do it all while scrutinizing our business with laser focus. You will help create everything from customer-facing and vendor-facing websites to the internal software and tools behind the reverse-logistics process. You can develop scalable, high-availability solutions to solve complex and broad business problems. We are a group that has fun at work while driving incredible customer, business, and environmental impact. We are backed by a strong leadership group dedicated to operational excellence that empowers a reasonable work-life balance. As an established, experienced team, we offer the scope and support needed for substantial career growth. Amazon is earth’s most customer-centric company and through WWR&R, the earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce team!
US, WA, Seattle
Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences
US, CA, Palo Alto
Amazon’s Advertising Technology team builds the technology infrastructure and ad serving systems to manage billions of advertising queries every day. The result is better quality advertising for publishers and more relevant ads for customers. In this organization you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), one of the world's leading companies. Amazon Publisher Services (APS) helps publishers of all sizes and on all channels better monetize their content through effective advertising. APS unites publishers with advertisers across devices and media channels. We work with Amazon teams across the globe to solve complex problems for our customers. The end results are Amazon products that let publishers focus on what they do best - publishing. The APS Publisher Products Engineering team is responsible for building cloud-based advertising technology services that help Web, Mobile, Streaming TV broadcasters and Audio publishers grow their business. The engineering team focuses on unlocking our ad tech on the most impactful Desktop, mobile and Connected TV devices in the home, bringing real-time capabilities to this medium for the first time. As a successful Data Scientist in our team, · You are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, and can credibly interface between technical teams and business stakeholders. You will collaborate directly with product managers, BIEs and our data infra team. · You will analyze large amounts of business data, automate and scale the analysis, and develop metrics (e.g., user recognition, ROAS, Share of Wallet) that will enable us to continually measure the impact of our initiatives and refine the product strategy. · Your analytical abilities, business understanding, and technical aptitude will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future. · You will have direct exposure to senior leadership as we communicate results and provide scientific guidance to the business. Major responsibilities include: · Utilizing code (Apache, Spark, Python, R, Scala, etc.) for analyzing data and building statistical models to solve specific business problems. · Collaborate with product, BIEs, software developers, and business leaders to define product requirements and provide analytical support · Build customer-facing reporting to provide insights and metrics which track system performance · Influence the product strategy directly through your analytical insights · Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
US, WA, Bellevue
mmPROS Surface Research Science seeks an exceptional Applied Scientist with expertise in optimization and machine learning to optimize Amazon's middle mile transportation network, the backbone of its logistics operations. Amazon's middle mile transportation network utilizes a fleet of semi-trucks, trains, and airplanes to transport millions of packages and other freight between warehouses, vendor facilities, and customers, on time and at low cost. The Surface Research Science team delivers innovation, models, algorithms, and other scientific solutions to efficiently plan and operate the middle mile surface (truck and rail) transportation network. The team focuses on large-scale problems in vehicle route planning, capacity procurement, network design, forecasting, and equipment re-balancing. Your role will be to build innovative optimization and machine learning models to improve driver routing and procurement efficiency. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. You will operate as part of a team of innovative, experienced scientists working on optimization and machine learning. You will work in close collaboration with partners across product, engineering, business intelligence, and operations. Key job responsibilities - Design and develop optimization and machine learning models to inform our hardest planning decisions. - Implement models and algorithms in Amazon's production software. - Lead and partner with product, engineering, and operations teams to drive modeling and technical design for complex business problems. - Lead complex modeling and data analyses to aid management in making key business decisions and set new policies. - Write documentation for scientific and business audiences. About the team This role is part of mmPROS Surface Research Science. Our mission is to build the most efficient and optimal transportation network on the planet, using our science and technology as our biggest advantage. We leverage technologies in optimization, operations research, and machine learning to grow our businesses and solve Amazon's unique logistical challenges. Scientists in the team work in close collaboration with each other and with partners across product, software engineering, business intelligence, and operations. They regularly interact with software engineering teams and business leadership.
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! In Prime Video READI, our mission is to automate infrastructure scaling and operational readiness. We are growing a team specialized in time series modeling, forecasting, and release safety. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment. As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.
US, WA, Seattle
Amazon's Weblab team enables experimentation at massive scale to help Amazon build better products for customers. A/B testing is in Amazon's DNA and we're at the core of how Amazon innovates on behalf of customers. We are seeking a skilled and experienced Research Scientist to help us build the future of experimentation systems at Amazon. About you: You have an entrepreneurial spirit and want to make a big impact on Amazon and its customers. You enjoy enabling intelligent decisions in the face of real-world, noisy data, and have a bias for delivering simple solutions to complex problems. You're a scientist looking for a career where you'll be able to build, to deliver, and to impress. You're a thought leader and you demonstrate this by delivering solutions, not just by having ideas. You challenge yourself and others to come up with better solutions. You develop strong working relationships and thrive in a collaborative and friendly team environment. About us together: We're going to help Amazon innovate faster and smarter by designing and delivering the next generation of data analysis tools and visualizations. Along the way, we're going to face seemingly insurmountable challenges. We're going to argue about how to solve them, and we'll work together to find a solution that is better than each of the proposals we came in with. We'll make complex decisions, but we'll all understand why. We'll be the dream team. We have decades of combined experience on the team in many areas of data science so it's a great environment in which to learn and grow. Many of us have university teaching experience and we're happy to teach. But, we're also like children playing at the edge of a vast unexplored territory; we have a lot to learn and we are all exploring together with a sense of awe and humility.
CA, ON, Toronto
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As a Sr. Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.
US, CA, San Francisco
The Amazon AGI SF Lab is focused on developing new foundational capabilities for enabling useful AI agents that can take actions in the digital and physical worlds. In other words, we’re enabling practical AI that can actually do things for us and make our customers more productive, empowered, and fulfilled. The lab is designed to empower AI researchers and engineers to make major breakthroughs with speed and focus toward this goal. Our philosophy combines the agility of a startup with the resources of Amazon. By keeping the team lean, we’re able to maximize the amount of compute per person. Each team in the lab has the autonomy to move fast and the long-term commitment to pursue high-risk, high-payoff research. If you’re interested in our particular philosophy of AI progress, reach out via AGI-SFLab-Jobs@amazon.com. Key job responsibilities - Develop cutting edge multimodal Large Language Models (LLMs) to observe, model and derive insights from manual workflows for automation - Work in a joint scrum with engineers for rapid invention, develop cutting edge automation agent systems, and take them to launch for millions of customers - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think big about the arc of development of GenAI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team
AT, Graz
Are you a MS or PhD student interested in a 2025 Internship in the field of machine learning, deep learning, speech, robotics, computer vision, optimization, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships.