Amazon Scholar solves century-old problem with automated reasoning

Solution method uses new infrastructure that reduces proof-checking overhead by more than 90%.

Marijn Heule, an Amazon Scholar and professor of computer science at Carnegie Mellon University, together with his colleague Manfred Scheucher of Technische Universität Berlin, have solved a geometry problem posed almost 100 years ago by the Hungarian-Australian mathematician Esther Szekeres.

Marijn Heule, an Amazon Scholar and professor of computer science at Carnegie Mellon University.

Paul Erdős, the legendary Hungarian mathematician who gave his name to the Erdős number, dubbed it the “happy-ending problem”, because work on it led to the marriage of Esther, née Klein, and Erdős’s long-time collaborator George Szekeres.

The problem asks the minimum number of points in a plane, no three of which are collinear, required to guarantee that n of the points constitute a convex polygon that does not contain any of the other points. (“Convex” means that a line segment connecting any two points within the polygon itself lies entirely within the polygon.)

Esther Szekeres dispatched the case of n = 4 in the 1930s. It was almost 50 years before Heiko Harborth determined that 10 points are needed to guarantee an empty pentagon. Around the same time, Joseph Horton showed that the problem is insoluble for polygons with seven or more sides: no number of points will guarantee that a convex 7-gon can be found that contains no other points in the collection.

But the remaining case — the empty hexagon — was still outstanding. That’s the problem that Heule and Scheucher solved. They showed that 30 points is sufficient to guarantee a convex hexagon that doesn’t contain any of the other points.

To prove this result, Heule and Scheucher used a SAT solver, an automated-reasoning tool that determines whether long chains of logical constraints can be satisfied. The SAT solver generates a proof that particular assignments of values to variables are prohibited by the constraints. Verifying the correctness of the proof requires another automated-reasoning tool, a proof checker.

Related content
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.

Proofs, however, can be hundreds of terabytes in size, and just managing input-output (I/O) and data retrieval during the proof-checking process can be hugely time consuming. “The cost of checking can be, say, 100% to 200% of the original solving time,” Heule says.

Heule, who is a member of Amazon Web Services’ (AWS’s) Automated Reasoning group, worked with his AWS colleagues to develop the infrastructure for a new streaming approach to proof checking, where a dedicated server core checks the proof as it is generated. This reduces the proof-checking overhead from 100% to 200% to somewhere around 10%.

This innovation, in turn, will be of use to the Automated Reasoning group in its future work on, say, software security, provably correct software, and hardware validation. Of course, those applications still require developers to create rigorous formal models of the systems they’re validating. But during the proof-checking phase, “if we can do things with say 10% overhead instead of 150%, that's a clear win,” Heule says.

Geometric constraints

SAT problems are NP-complete, meaning that SAT problems can be devised that would be insoluble by all the computers in the world in the lifetime of the universe.

But that doesn’t mean that all SAT problems, or even SAT problems with large numbers of variables, are insoluble, and part of the automated-reasoning researcher’s art is formulating problems in such a way that a SAT solver can solve them.

“Marijn is best-in-the-world at mapping complex problems to solvers,” says Robert Jones, a senior principal applied scientist in the AWS Automated Reasoning group.

Related content
CAV keynote lecture by the director of applied science for AWS Identity explains how AWS is making the power of automated reasoning available to all customers.

The setup of the happy-ending problem can be described using binary (Boolean) variables each of which describes the orientations of three points. The variables all have the same general form: given three points in general position (i.e., not collinear), A, B, and C, C is above the line through A and B. (If the variable is false, C is necessarily below the line.) Chain enough of these together, and you can specify the 30 points of the 6-gon case (or 29 points, or any other number).

Within that framework, the difficulty is to describe the condition that there be at least one hexagon with no point inside it. Scheucher’s group had been batting that problem about for years without arriving at a formulation that a SAT solver could handle. That’s where Heule came in.

People mapping problems to SAT expressions often focus on concision, Heule explains; the more concise the expression, they reason, the fewer possibilities the solver will need to consider. That may be true in general, Heule says, but in his experience, long chains of simple constraints are often easier to reason about than short chains of more complex constraints.

Simplifying the problem

The natural way to approach the empty-hexagon problem is to break hexagons into triangles and reason about whether each triangle has a point in its interior. Prior attempts to map this problem to a SAT expression had taken a general approach, specifying a set of logical constraints that could be applied to any triangle in the collection and all hexagons that included that triangle. The resulting expression, Heule says, was easy to formulate but hard to reason about.

Heule suggested that he and Scheucher take the opposite tack, explicitly labeling every possible configuration of each hexagon, specifying the individual triangles using those labels, and checking each of the named triangles for points in its interior.

Three hexagons, with vertices labeled with the letters a through f. Each hexagon is divided into four triangles — one "inner" triangle, which shares all of its sides with other triangles, and three "outer" triangles. In all three triangles, the line segment af is the longest line segment connecting any two vertices. In the first hexagon, no vertices are below the line segment af; in the second triangle, one vertex is; and in the third triangle, two vertices are.
These three hexagons differ in the number of points that lie below the line segment af. Any other arrangement of points can be mapped to one of these structures. In all three hexagons, establishing that the central (pink) triangle is empty is sufficient to conclude that the point set contains an empty hexagon.

“In this case, you really need to blow it up in order to get much smaller later,” Heule explains. “I made it 10 times bigger and afterward realized that the new expression could be compressed substantially. This compression step is also possible with existing automated-reasoning tools.”

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

One of the ways that SAT solvers reduce the complexity of the problems they’re tackling is by looking for logical redundancies and removing them. In his initial specification of the empty-hexagon problem, Heule divided each hexagon in the point set into four triangles and checked each triangle for a point in its interior.

He noticed, however, that the SAT solver reduced this step to checking only one triangle per hexagon. After thinking it through, Heule and Scheucher realized that in each hexagon, there was a single triangle — call it the inner triangle — that shared all its sides with the hexagon’s other three triangles — call them the outer triangles. If that inner triangle was empty, then it was possible to deduce the existence of an empty hexagon from the points in the point set.

Suppose that one of the outer triangles contains a point. Then it’s possible to draw a new triangle that contains that point and shares a side with the inner triangle. Repeating this process as needed is guaranteed to yield a convex hexagon with no points in its interior.

An animation that begins with a blue hexagon divided into four triangles, one "inner triangle" that shares all its sides with other triangles and three "outer triangles". Two of the outer triangles enclose dots. First, the inner triangle turns orange. Then, two dotted lines connect each dot with the two corners of the corresponding outer triangle that are shared by the inner triangle. The dotted lines solidify, creating a new hexagon, and the sides of the old hexagon dissolve. The new hexagon turns orange.
In a hexagon constructed from points in a prespecified set, if any of the "outer triangles" enclose points in the set, it's possible to draw a new hexagon — still constructed from the same set — that does not enclose them.

Heule and Scheucher extracted this line of reasoning from the SAT solver itself. “I have frequently seen that the solver provides useful feedback, although it's feedback for an expert,” Heule says. “I think it's really important that this feedback becomes available for nonexperts. For example, you implement something, and the solver says, ‘Okay, you're trying to do this, but that part of the expression is not needed.’ This feedback can be used to reformulate the expression in such a way that that it is much easier to solve.”

Related content
Method enables machine-checkable proofs of SAT solvers’ decisions on incremental SAT problems, in which problem constraints are gradually imposed over time.

Once Heule and Scheucher understood what the solver was telling them, they were able to devise a more practical specification of the SAT problem. The solver was able to reason through all the possibilities for a 30-point point set and prove that, within that set, there must exist at least one hexagon whose inner triangle contained no other points.

It was still an extremely long proof, but Heule and his AWS colleagues’ new proof-checking mechanism was able to confirm its validity relatively quickly.

“One of the issues here is that many users of these tools don't know how to get the most out of them,” Heule says. “And that's not only for this specific problem but for many other problems as well. Within Amazon, there are a lot of applications where SAT solvers could verify developers’ work or find better solutions. I can help by writing an effective encoding, but ideally, everything would be done automatically. I would love to see myself being taken out of the equation.”

Research areas

Related content

US, CA, San Francisco
If you are interested in this position, please apply on Twitch's Career site About Us: Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and Twitter, and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process. About the Role: We are looking for an Applied Scientist to solve challenging and open-ended problems in the domain of recommendations, search, ranking and information retrieval. As an Applied Scientist on Twitch's Community team, you will use ML to help viewers find streamers and communities they’ll love. You will collaborate with a team of passionate scientists and engineers to develop these models and put them into production, where they can help Twitch's creators and viewers succeed and build communities. You will report to the Applied Science Manager on the Community Discovery Team. This position is located in San Francisco, CA. You Will: - Develop and Productionize ML algorithms for recommendations, ranking and search problems that can improve discovery on Twitch. - Collaborate with our Product and Engineering teams to work backwards from customer discovery problems, to determine the ML solution (algorithm and pipeline) to have the biggest impact on our user base in the real world. - Participate in the scientific community at Twitch, Amazon, and the broader ML and risk community. Perks - Medical, Dental, Vision & Disability Insurance - 401(k) - Maternity & Parental Leave - Flexible PTO - Amazon Employee Discount
US, WA, Bellevue
We are building a world-class last mile delivery ecosystem with Amazon Flex as a cornerstone of this strategy. Amazon Flex works directly with independent contractors, to make deliveries to our customers. With Amazon Flex, delivery partners are their own boss, build their own schedule, and choose from different types of delivery opportunities (e.g. Amazon Fresh, Whole Foods Market, and Amazon Logistics). Amazon Flex is powered by a mobile app that works in sync with our advanced systems and processes, allowing delivery partners to secure delivery offers, track their delivery progress, and more. Economists at Amazon Flex partner closely with senior management, business stakeholders, scientists and engineers, and economist leadership to solve key business problems including pricing, promotions, offer optimization, recruiting, capacity planning, and beyond. Amazon Flex Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical labor, or related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of a cross-functional team that supports all of Amazon Last Mile Delivery Tech. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems across the business.
US, GA, Atlanta
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real- world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution A day in the life N/A 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 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.
US, WA, Bellevue
We are a part of Amazon Alexa Devices organization with the mission “delight customers through contextual and personalized proactive experiences that keep customers informed, engaged, and productive without cognitive burden”. We are developing an advanced system using Large Language Model (LLM) technologies to deliver engaging, intuitive, and adaptive content recommendations across all Amazon surfaces. We aim to facilitate seamless reasoning and customer experiences, surpassing the capabilities of previous machine learning models. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware speech assistant. 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 enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, shipping solutions via rapid experimentation and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist on the team, you will collaborate with other applied scientists and engineers to develop novel algorithms to enable timely, relevant and delightful recommendations and conversations. Your work will directly impact our customers in the form of products and services that make use of various machine learning, deep learning and language model technologies. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in the state of art.
US, WA, Bellevue
The Fulfillment by Amazon (FBA) team is looking for a passionate, curious, and creative Research Scientist, with expertise and experience in operations research, operations management, supply chains, and revenue management, to join our top-notch cross-domain FBA science team. As a research scientist you will be responsible for designing and implementing cutting edge optimization models and machine learning models and building automated inventory management system to solve key challenges facing the worldwide FBA Seller business, including 1) improving FBA Seller inventory efficiency, 2) efficiently balancing the supply and demand of FBA Seller capacity, 3) closing worldwide selection gap by enabling global selling profitability, and 4) driving out costs across the FBA supply chain to spin the flywheel. Unlike many companies who buy existing off-the-shelf planning systems, we are responsible for studying, designing, and building systems to suit Amazon’s needs. Our team members have an opportunity to be on the forefront of thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, research scientists/statisticians/economists and software developers in the business. This role will work with other senior and principal scientists, and partner with engineering and product teams to integrate scientific work into production systems. Key job responsibilities • Interact with engineering, operations, science and business teams to develop an understanding and domain knowledge of processes, system structures, and business requirements • Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization • Develop scalable mathematical models to derive optimal or near-optimal solutions to existing and new inventory planning challenges • Create prototypes and simulations to test devised solutions • Advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers • Work closely with engineers to integrate prototypes into production systems • Create policy evaluation methods to track the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features A day in the life As a Research Scientist, you will solve real world large inventory problems by analyzing large amounts of business data, defining new metrics and business cases, designing simulations and experiments, applying supply chain modeling techniques, creating optimization models, and collaborating with teammates in business, software, and research. The successful candidate has solid research experience in Operations Research preferably with focus on Operations Management or other closely related areas or in area of Machine Learning. He or she will lead the research where we are responsible for developing solutions to better manage and optimize worldwide FBA inventory capacity, while providing the best experience to our Sellers to growth their business. About the team Fulfillment by Amazon (FBA) is a service that allows sellers to outsource order fulfillment to Amazon, allowing sellers to leverage Amazon’s world-class facilities to provide customers Prime delivery promise. Sellers gain access to Prime members worldwide, see their sales lift, and are free to focus their time and resources on what they do best while Amazon manages fulfillment. Over the last several years, sellers have enjoyed strong business growth with FBA shipping more than half of all products offered by Amazon. FBA focuses on helping sellers with automating and optimizing the third-party supply chain. FBA sellers leverage Amazon’s expertise in machine learning, optimization, data analytics, econometrics, and market design to deliver the best inventory management experience to sellers. We work full-stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making.
US, WA, Seattle
Are you passionate about delighting hundreds of millions of customers and building the best search experience to help customers make well-informed purchase decisions on Amazon? Are you passionate about building the next generation product shopping and search experience? The Search and Discover experience on Amazon is central to every customer’s shopping mission and purchasing journey. Amazon Search is looking for a self-driven, customer obsessed, and seasoned research scientist to drive the overall search customer insights efforts and measure customer perceptions for Amazon Search. If you are passionate about using user research & customer insights to influence the future direction of Amazon Search and building a small but top notch user research science team, this is a job for you. In this highly visible role, you will work across cross-functional teams and collaborate with partners to drive user research planning, align research goals to the product roadmap, and own user research execution and final deliverables to make sure that we are always positioned to exceed customer expectations. You will present the search customer insights to various stakeholders including senior executives. Key job responsibilities * Design and conduct significantly complex research studies that impact long-term product strategy and the future of customer experience. * Build customer perception measurements for Amazon search experience and develop the methods to correlate customer perception with search experience improvements. * Define search customer insights research strategy, own the research roadmap and prioritize research opportunities across different areas. * Identify customer segments and latent customer needs, define and improve methodologies, data collection, analysis/synthesis, and identify opportunities to improve customer experience. * Manage multiple customer insights research project execution, prioritization, and ensure research projects timely delivery at the highest quality levels. * Adapt and/or create new customer insights research methodologies and workflows to support product goals at scale and work effectively with agencies and vendors. * Work cross functionally and collaborate with technical product managers, technical program managers, UX designers, science, and engineering teams to proactively plan research and align research goals to the product roadmap. * Work with data analysts/data scientists to correlate qualitative research with quantitative data analysis, and interpret complicated data across quantitive and behavioral analysis. * Own customer insights research results and prioritization communication with all stakeholders including senior executives. * Build, manage, and grow a small team of research scientists. About the team Our team operate in a friendly, fast-paced, and diverse and inclusive work environment. We are driven by the excitement of inventing products, building technologies, and providing services that change lives. We embrace new ways of doing things, make decisions quickly, and are not afraid to fail. We have the scope, benefits, and support of a large company and the spirit and heart of a small startup. At Amazon, our mission is to be Earth’s most customer-centric company. Our actions, goals, projects, programs, and inventions begin and end with the customer top of mind.
US, WA, Seattle
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Creative X org within Amazon Advertising aims to democratize access to high-quality creative assets, including copy, images and video, by building and productizing generative AI-driven tools for advertisers. We are investing in latent-diffusion and DiT models, LLMs, computer vision, reinforcement learning, and image + video synthesis. The solutions we develop will be deployed for use by self-service advertisers and agencies, as well as available to premium brands that advertise on Amazon. We are seeking an experienced science leader who is adept at a variety of skills; especially in generative AI, computer vision, and large language models that will accelerate our plans to generate high-quality creatives on behalf of advertisers. The right candidate will be an inventor at heart, provide science leadership, establish the right direction and vision, build team mechanisms, foster the spirit of collaboration and innovation within the org, and execute against a roadmap. The leader will provide both technical direction as well as manage a sizable team of scientists. They will need to be adept at recruiting, launching AI models into production, writing vision/direction documents, and building team mechanisms that will foster innovation and execution. Key job responsibilities * Drive end-to-end applied science projects that have a high degree of ambiguity, scale, complexity * Provide technical / science leadership related to computer vision, large language models, and generative image + video. * Research new and innovative machine learning approaches. * Recruit high performing Applied Scientists to the team and provide mentorship. * Establish team mechanisms, including team building, planning, and document reviews.
CA, BC, Vancouver
Technology is giving the beauty industry a makeover! Are you interested to disrupt and redefine the way customers buy Beauty products online? Are you interested in using the latest advances in machine learning, computer vision, and big-data technologies to build online customer experiences for Beauty products that can equal or even surpass an in-store experience? Amazon Beauty is reinventing the shopping experience for all beauty customers across the largest selection of brands to become the most trusted beauty destination. Beauty is unique in retail with a diverse customer set along with products that are emotional, fun, and creative. This is your chance to get in on the ground floor to build something entirely new and transform an industry! To achieve our vision, we think big and tackle technological challenges every day. We need builders and disruptors who are not afraid to innovate! Our architecture and development processes support rapid experimentation, global deployments, and self-service capabilities that allow us to scale better. We build: - Amazon scale systems: All our technology needs to work at Amazon scale, serving millions of customers with millisecond-level latency. - Immersive customer experiences: We will create elevated and immersive customer experiences that using cutting-edge UI-technologies and user-centric design patterns. - Computer Vision and augmented reality (AR) experiences: We bring exciting experiences directly to the customer's mobile phone using their cameras and combinations of computer vision and AR. - Personalization using machine learning: We use latest advances in ML and GenAI to provide better-personalized shopping experiences. - Data & analytics pipelines: Amazon is data-driven, and a robust data backbone is necessary for our systems. We build on core AWS services such as EC2, S3, DynamoDB, SageMaker, StepFunctions, etc. - Multi-device support: We build for all traditional surfaces - desktop browsers, mobile browsers, and mobile applications. Key job responsibilities We are looking for talented and innovation-driven scientists who are passionate about leveraging the latest advances in Generative AI, Diffusion Models, Computer Vision (CV), Graphics, AR/VR, Virtual Try-On, Image Processing, and related technologies, to solve customer problems in the Beauty space. You will have an opportunity to revolutionize the customer shopping experience across the world's most extensive catalog of beauty products. You will be directly responsible for leading the ideation, design, prototyping, development, and launch of innovative scientific solutions that address customer problem in the beauty and shopping space. You will closely partner with product managers, UX designers, engineers, and the broader Amazon scientific community to pioneer state-of-the-art solutions to extremely challenging problems in machine learning and CV. You will be our organization's Tech Evangelist and represent our organization in key internal and external AI, ML, or Vision conferences. About the team Amazon Beauty Tech is a key and essential part of the Consumables organization and North America Stores. We are a passionate group of engineers, scientists, product managers, and designers who drive technological innovation to improve the customer shopping experience. We have a startup-like work culture where innovation is encouraged; we are never afraid to propose big ideas for fear of failing!
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and models for supervised fine-tuning and reinforcement learning through human feedback; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative Artificial Intelligence (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - 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
US, CA, Santa Clara
Amazon AI is looking for world class scientists to join its Amazon Q Builder CodeGen team. Amazon Q Builder CodeGen is an LLM-based AWS service that makes developers more productive by providing them code recommendations. Amazon Q Builder CodeGen leverages large language models, program analysis, responsible AI, robustness, efficient inference techniques and a lot more in building this technology. You will invent, implement, and deploy state of the art algorithms and systems, and be at the heart of a growing and exciting focus area for AWS. Candidate experiences of interest include but are not limited to: LLM, RAG, model training and inference, trustworthy AI, responsible AI, program analysis and program synthesis in general. The Amazon Web Services (AWS) Next Gen DevX (NGDE) team uses generative AI and foundation models to reimagine the experience of all builders on AWS. From the IDE to web-based tools and services, AI will help engineers work on large and small applications. We explore new technologies and find creative solutions. Curiosity and an explorative mindset can find a place here to impact the life of engineers around the world. If you are excited about this space and want to enlighten your peers with new capabilities, this is the team for you. About the team 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. 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.