Automated reasoning at Amazon: A conversation

To mark the occasion of the eighth Federated Logic Conference (FloC), Amazon’s Byron Cook, Daniel Kröning, and Marijn Heule discussed automated reasoning’s prospects.

The Federated Logic Conference (FLoC) is a superconference that, like the Olympics, happens every four years. FLoC draws together 12 distinct conferences on logic-related topics, most of which meet annually. The individual conferences have their own invited speakers, but FLoC as a whole has several plenary speakers as well.

At the last FLoC, in 2018, one of those plenary speakers was Byron Cook, who leads Amazon’s automated-reasoning group, and he was introduced by Daniel Kröning, then a professor of computer science at the University of Oxford

Byron Cook's keynote at FLoC 2018
With introduction by Daniel Kröning.

“What makes me so proud that Byron is here,” Kröning said, is “he’s now at Amazon, and he’s going to run the next Bell Labs, he’s going to run the next Microsoft Research, from within Amazon. My prediction is that — not 10 years but 16 years; remember, it’s multiples of four — 16 years from now you’ll be at a FLoC, and you’ll hear these stories about the great thing that Byron Cook built up at Amazon Web Services. And we’ll speak about it in the same tone as we’re now talking about Bell Labs and Microsoft Research.”

In the audience at the talk was Marijn Heule, a highly cited automated-reasoning researcher who was then at the University of Texas.

“I hadn't met Marijn, though I had heard about him from a couple other people and thought I should talk to him,” Cook says. “And then Marijn found me at the banquet after the talk and was like, ‘I want a job.’”

AR scientists.png
L to R: Amazon vice president and distinguished scientist Byron Cook; Amazon Scholar Marijn Heule; Amazon senior principal scientist Daniel Kröning.

Heule is now an Amazon Scholar who divides his time between Amazon and his new appointment at Carnegie Mellon University. Kröning, too, has joined Amazon as a senior principal scientist, working closely with Cook’s group.

As 2022’s FLoC approached, Cook, Kröning, and Heule took some time to talk with Amazon Science about the current state of automated-reasoning research and its implications for Amazon customers.

Related content
Meet Amazon Science’s newest research area.

Amazon Science: The conference name has the word “logic” in it. Does FLoC deal with other aspects of logic, or is logic coextensive with automated reasoning now?

Byron Cook: It’s about the intersection of logic and computer science. Automated reasoning is one dimension of that intersection.

Daniel Kröning: Traditionally, FLoC is split into two halves, with the first half more theoretical and the second half more applied.

Cook: One of the things about automated reasoning is you're on the bleeding edge of what is even computable. We're often working on intractable or undecidable problems. So people automating reasoning are really paying attention to both the applied and the theoretical.

AS: I know Marijn is concentrating on SAT solvers, and SAT is an intractable problem, right? It’s NP-complete?

Marijn Heule: Yes, but you can also use these techniques to solve problems that go beyond NP. For example, solvers for SAT modulo theories, called SMT. I even have a project with one student trying to solve the famous Collatz conjecture with these tools.

Collatz-27.png
The Collatz conjecture posits that any integer will be transformed into the integer 1 through iterative application of two operations: n/2 and 3n+1. This figure shows a "Collatz cascade" of possible transitions from 27 to 1 using a set of seven symbols, which can be interpreted as simple calculations, and 11 rules for transforming those symbols into symbols consistent with the Collatz operations. At top right are the symbol rewrite rules; at bottom left is a blowup of part of the cascade, illustrating sequences of rewrites that yield the number 425 and its transformation through Collatz operations.

Kröning: SAT is now the inexpensive, easy-to-solve workhorse for really hard problems. People still have it in their heads that SAT equals NP hard, therefore difficult to solve or impossible to solve. But for us, it's the lowest entry point. On top of SAT, we build algorithms for solving problems that are way harder.

Cook: One of the tricks of the trade is abstraction, where you take a problem that's much, much bigger but represent it with something smaller, where classes of questions you might ask about the smaller problem imply that the answer also holds for the bigger problem. We also have techniques for refining the abstractions on demand when the abstraction is losing too much information to answer the question. Often we can represent these abstractions in tools for SAT.

Related content
Distributing proof search, reasoning about distributed systems, and automating regulatory compliance are just three fruitful research areas.

Marijn’s work on the Collatz conjecture is a great example of this. He has done this amazing reduction of Collatz to a series of SAT questions, and he's tantalizingly close to solving it because he's got one decidable problem to go — and he's the world expert on solving those problems. [Laughs]

Heule: Tantalizingly close but also so far away, right? Because this problem might not be solvable even with a million cores.

Cook: But it's still decidable. And one of the thresholds is that NP, PSpace, all these things, they're actually decidable. There are questions that are undecidable — and we work on those, too. When a problem is undecidable, it means that your tool will sometimes fail to find an answer, and that's just fundamental: there are no extra computers you could use ever to solve that. The halting problem is a great example of that.

Heule: For these kinds of problems, you're asking the question “Is there a termination argument of this kind of shape?” And if there is one, you have your termination argument. If there is no termination argument of that shape, there could be one of another shape. So if the answer is SAT [satisfiable], then you're happy because you’ve solved the problem. If the answer is no, you try something else.

Cook: It's really, really exciting. In Amazon, we're building these increasingly powerful SAT solvers, using the power of the cloud and distributed systems. So there's no better place for Marijn to be than at Amazon.

Related content
ICSE paper presents techniques piloted by Amazon Web Services’ Automated Reasoning team.

AS: Daniel, could we talk a little bit about your research?

Kröning: What I'm looking at right now is reasoning about the cloud infrastructure that performs remote management of EC2 instances — how to secure that in a way that is provable. You also want to do that in a way that is economical.

Cook: One of the things that Daniel's focusing on is agents. We have pieces of software that run on other machines, like EC2 instances, agents for telemetry or for control, and you give them power to take action on your behalf on your machine. But you want to make sure that an adversary doesn't trick those agents into doing bad things.

Correct software

AS: I know that, commercially, formal methods have been used in hardware design and transportation systems for some time. But it seems that they’re really starting to make inroads in software development, too.

The storage team is able to write code that otherwise they might not want to deploy because they wouldn't be as confident about it, and they're deploying four times as fast. It was an investment in agility that's really paid off.
Byron Cook

Cook: The thing we've seen is it's really by need. The storage team, for example, is able to be much more agile and be much more aggressive in the programs that they write because of the formal methods. They're able to write code that otherwise they might not want to deploy because they wouldn't be as confident about it, and they're deploying four times as fast. It was an investment in agility that's really paid off.

Kröning: There are actually a good number of stories wherein engineering teams didn't dare to roll out a particular feature or design revision or design variant that offers clear benefits — like being faster, using less power — because they just couldn't gain the confidence that it's actually right under all circumstances.

Heule: The interesting thing is that you even see this now in tools. Now we have produced proofs from the tools, and people start implementing features that they wouldn't dare have in the past because they were not clear that they were correct. So the solvers get faster and more complex because we now can check the results from the tools and to have confidence in their correctness.

Related content
SOSP paper describes lightweight formal methods for validating new S3 data storage service.

Cook: Yeah, I wanted to double down on that point. There’s a distinction in automated reasoning between finding a proof and checking your proof, and the checking is actually relatively easy. It's an accounting thing. Whereas finding the proof is an incredibly creative activity, and the algorithms that find proofs are mind-blowing. But how do you know that the tool that found the proof is correct? Well, you produce an auditable artifact that you can check with the easy tool.

SAT in the cloud

AS: What are you all most excited about at this year’s FLoC?

Cook: The SAT conference is at FLoC, and there will be the SAT competition results, and one of the things I'm really excited about is the cloud track. Automated reasoning has really moved into the cloud, and the past couple years running the cloud track has really blown the doors off what's possible. I'm expecting that that will be true again this year.

SAT results.png
The results of the top-performing cloud-based, parallel, and sequential SAT solvers in this year's SAT competition, whose results were presented at FLoC. The curves show the number of problems (y-axis) in the SAT competition's anniversary problem set — which aggregates all 5,355 problems presented in the competition's 20-year history — that a given solver could solve in the allotted time (x-axis).

Heule: This is the first year that Amazon is running both the parallel track and the cloud track, and the cloud track was only possible because of Amazon. Before that, there was no way we had the resources to run a cloud track. In the cloud track, every solver-benchmark combination is run on 1,600 cores. And this year is extra special because it's 20 years of SAT, and we have a single anniversary track and all the competitions that were run in the past are in there. That is 5,355 problems, and all the solvers are running on this.

Cook: Wow.

Heule: I'm also excited to see the results. We have seen in the last year and the year before that the cloud solver can, say, solve in 100 seconds as much as the sequential solvers can do in 5,000 seconds. The user doesn't have to wait for four hours but just for four minutes

Cook: And that raises all boats because, as we mentioned earlier, everything is reduced to SAT. If the SAT solvers go from one hour to one minute, that's really game changing. That means a whole other set of things you can do.

What has been clear for a while but continues to be true is there's some sort of Moore's-law thing happening with SAT. You fix the same hardware, the same benchmarks, and then run all the tools from the past 20 years, and you see every year they're getting dramatically better. What's also really amazing is that in many ways the tools are getting simpler.

LH: Are the simplicity and efficiency two sides of the same coin? Understanding the problems better helps you find a simpler solution, which is more efficient?

Cook: Yeah, but it’s also the point that Marijn made that because the tools produce auditable proofs that you can check independently, you can do aggressive things that we were scared to do before. Often, aggressive is much simpler.

Related content
Automated-reasoning method enables the calculation of tight bounds on the use of resources — such as computation or memory — that results from code changes.

Heule: It's also the case that we now understand there are different kinds of problems, and they need different kinds of heuristics. Solvers are combining different heuristics and have phases: “Let's first try this. Let's also try that.” And the code involved in changing the heuristics is very small. It's just changing a couple of parameters. But if you notice, okay, this set of heuristics works well for this problem, then you kind of focus more on that.

Cook: One of the things a SAT solver does is make decisions fast. It just makes a bunch of choices, and those choices won't work out, and then it spends some time to learn lessons why. And then it has a very efficient internal database for managing what has been learned, what not to do in the future. And that prunes the search space a lot.

One of the really exciting things that's happening in the cloud is that you have, say, 1,000 SAT solvers all running on the same problem, and they're learning different things and can share that information amongst them. So by adding 5,000 more solvers, if you can make the communication and the lookup efficient between them, you're really off to the races.

The other thing that's quite neat about that is the point that Marijn is making: it's becoming increasingly clear that there are these fundamental building blocks, and for different kinds of problems, you would want to use one kind of Lego brick versus a different kind of Lego brick. And the cloud allows you to run them all but then to share the information between them.

Iterated SAT solver.png
In "Migrating solver state", Heule and his colleagues show that passing modified versions of a problem between different solvers can accelerate convergence on a solution.

Heule: We have an Amazon paper at FLoC with some very cool ideas. If you run things in the cloud, you sometimes have a limited time window where you have to solve them, and otherwise it stops. You started with a certain problem, the solver did some modifications, and now we have a different problem. Initially we just tested, Okay, can we stop the solver and then store the modified problem somewhere and continue later, in case we need more time than we allocated initially? And then we can continue solving it.

But the interesting thing is that if you give the modified problem to another solver, and you give it, say, a couple of minutes, and then it stores the modified problem, and you give it to another solver, it actually really speeds things up. It turns out to solve the most instances from everything that we tried.

AS: Do you do that in a principled way, or do you just pick a new solver randomly?

Related content
In a pilot study, an automated code checker found about 100 possible errors, 80% of which turned out to require correction.

Heule: The thing that turned out to work really well is to take two top-tier solvers and just Ping-Pong the problem among them. This functionality of storing and continuing search requires some work, so that implementing it in, say, a dozen solvers would require quite some work. But it would be a very interesting experiment.

AS: I’m sure our readers would love to know the result of that experiment!

Well, thank you all very much for your time. Does anyone have any last thoughts?

Cook: I think I speak for the thousands of others who are attending FLoC: we are ready to having our minds blown, just as we did in 2018. Many of the tools and theories presented by our scientific colleagues at this year’s FLoC will challenge our current assumptions or spark that next big insight in our brains. We will also get to catch up with old friends that we’ve known for around 20 years and meet new ones. I’m particularly excited to meet the new generation of scientists who have entered the field, to see the world afresh through their eyes. This is such an amazing time to be in the field of automated reasoning.

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
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 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.
IL, Tel Aviv
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. Key job responsibilities PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience 3+ years of building models for business application experience Experience in patents or publications at top-tier peer-reviewed conferences or journals Experience programming in Java, C++, Python or related language Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
IL, Haifa
Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.
US, MA, Westborough
We are seeking a Principal Applied Scientist to lead the development of our autonomous driving stack for last-mile delivery vehicles. In this role, you will drive technical innovation, architect advanced autonomous systems, and lead a team of researchers and engineers in pushing the boundaries of what's possible in autonomous delivery. Key job responsibilities As the Principal Applied Scientist, you will architect and evolve LMDA's autonomous driving stack for last-mile delivery vehicles. Your role involves driving research and development in key areas such as perception, prediction, planning, and control. You will develop novel algorithms and approaches to solve complex challenges in urban autonomous navigation. A critical aspect of your role will be leading system-level architecture decisions and setting technical direction for the autonomous systems team. You will mentor and develop a team of scientists and engineers, fostering a culture of innovation and excellence. This involves close collaboration with cross-functional teams including hardware, safety, and operations to ensure seamless integration of autonomous systems. As a senior technical leader, you will represent LMDA's technical capabilities to partners, customers, and at industry conferences. In this role, you will define and execute the technical roadmap for LMDA's autonomous systems. This includes identifying key research areas and technological advancements that will drive LMDA's competitive advantage. A crucial aspect of your role will be balancing long-term research goals with near-term product delivery needs. You will lead the integration of various autonomous subsystems into a cohesive, performant stack. This includes developing and implementing strategies for optimizing system performance across hardware and software. You will also design and oversee testing and validation frameworks for autonomous systems. About the team Last Mile Delivery Automation (LMDA) is at the forefront of revolutionizing the logistics industry through advanced autonomous vehicle technology. Our mission is to create safe, efficient, and scalable autonomous solutions for last-mile delivery, reducing costs and environmental impact while improving delivery speed and reliability.
US, NY, New York
Join us in a historic endeavor to make Generative AI accessible to the world with breakthrough research! The AWS AI team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists drives the innovation that enables external and internal SageMaker customers to train their next generation models on both GPU and Trainium instances. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. 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. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. 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. 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. 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. 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.