Senior Applied Scientist, AWS Quick
About the role
Come join the AWS 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.
Key job responsibilities
- Design and implement scalable, production-grade neuro-symbolic systems that integrate formal reasoning with GenAI to deliver reliable, verifiable outcomes for AWS customers.
- Collaborate cross-functionally with product, engineering, and science teams as well as external customers to deeply understand pain points, gather requirements, and translate them into neuro-symbolic features that solve real-world problems.
- Enhance and extend the capabilities of formal reasoning systems to meet the demands of GenAI and agentic applications — including areas such as hallucination detection, policy verification, and automated guardrails.
- Proactively identify and pursue new opportunities to apply formal reasoning solutions across AWS services and customer domains, driving adoption and expanding the impact of neuro-symbolic approaches.
- Own the end-to-end science lifecycle — from research and experimentation through production deployment — defining metrics to measure system performance and the real-world impact of neuro-symbolic solutions.
- Mentor junior scientists and engineers, providing technical guidance, fostering a culture of scientific rigor, and raising the bar across the team.
- Advance the state of the art through publications at top-tier venues, patents, or open-source contributions, strengthening Amazon's position as a leader in automated reasoning and neuro-symbolic AI.
About the team
You will be working with a team of formal methods and machine learning specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of new features for Automated Reasoning checks that delight our customers.
Why AWS?
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 empowers us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon 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.
Basic Qualifications
- PhD, or Master's degree and 5+ years of applied research experience
- Experience programming in Java, C++, Python or related language
Preferred Qualifications
- Experience in formal verification, program analysis, constraint-solving, symbolic execution, model checking, SAT/SMT solver implementation and applications, mechanical theorem and/or code-reasoning languages such as Lean