Principal Applied Scientist - AI for Life Sciences, AWS Applied AI Solutions - Life Sciences

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset.

The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients.

We're out to revolutionize how medicines are discovered, developed, and brought to patients powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models large language models and agentic reasoning systems to life sciences problems then put them into the hands of pharma biotech and diagnostics customers as applications and managed services they can fine-tune tailor and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological regulatory and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains?

Today we're focused on two areas. In drug design, our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. In clinical trials we're building AI that automates and optimizes regulatory and clinical development workflows. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems.

We value scientific rigor encourage publication and support conference participation. If you want to do research that ships this is the team.

Key job responsibilities • Set the scientific vision and research agenda for LLM reasoning, agentic AI, and biological model customization across the portfolio • Innovate on LLM reasoning, planning, and agentic approaches for complex scientific and regulatory workflows • Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that enable customers to train better models on their own data with less effort • Advance methods to adapt and extend biological foundation models for customer-specific therapeutic applications • Solve open research problems in faithful reasoning, multi-step planning, and tool use in high-stakes scientific domains • Partner with Life Sciences domain experts and customers to understand their hardest scientific challenges and translate those into tractable research problems • Publish at top-tier venues and build the team's external scientific reputation • Mentor applied scientists across the team while maintaining significant personal research contribution • Collaborate with product and engineering to ensure research translates into shipped products that serve customers at scale • Influence multi-year research roadmaps through deep scientific expertise and customer understanding

A day in the life • Push a new reasoning approach into production that measurably improves outputs for a pharma customer's workflow • Design and run experiments to validate a novel fine-tuning method then ship it as a capability customers can use immediately • Unblock a delivery milestone by diagnosing why a model is failing on a new class of inputs and implementing a fix • Meet with a customer's scientific team to scope what the next model release needs to do for them • Review a teammate's experimental results sharpen the approach and help get it over the finish line • Publish results from shipped work at a top venue closing the loop between research and impact • Prototype a new idea that could become the next major capability in the product

About the team 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.

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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. - PhD in Machine Learning, Computer Science, Computational Biology, or related field - Experience as a senior or principal-level applied scientist with a track record of solving complex technical problems - Deep expertise in LLMs, reasoning systems, and agentic AI, including architecture design, training, fine-tuning, and optimization - Strong publication record at top-tier ML or computational biology venues - Demonstrated ability to innovate on model customization techniques (fine-tuning, RLHF, retrieval augmentation, domain adaptation) and make models work for specific downstream applications - Domain experience in life sciences, computational biology, or related scientific fields (protein engineering, antibody design, clinical data, or regulatory science) - Experience translating research into products or services that others use (not just standalone publications) - Experience building and launching 0-to-1 AI products or capabilities, from initial research through first customer delivery - Excellent programming skills in Python and deep learning frameworks (PyTorch, JAX), with a modern development practice that embraces AI-assisted coding and iteration - Experience building managed AI services or model-as-a-service offerings that scientists use directly - Deep expertise in protein structure prediction, inverse folding, or antibody optimization using AI - Demonstrated experience with clinical data standards or regulatory science - Experience with model customization at scale: efficient fine-tuning, RLHF, retrieval-augmented generation, or domain adaptation - A history of mentoring senior scientists and helping them achieve career breakthroughs

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