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.

The Role

We are seeking an exceptional Principal Applied Scientist to set the scientific direction for our life sciences AI portfolio. You will be the scientific leader who defines research agendas, architects novel approaches, and delivers models and methods that give our customers capabilities that did not previously exist.

This is a rare role that combines deep expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will innovate on how large language models reason, plan, and act in complex scientific domains, while applying domain knowledge in biology to ensure models produce scientifically valid outputs. The problems span multiple fronts:

- How do you build LLM-based agentic systems that correctly reason over clinical protocols, regulatory standards, and complex multi-step scientific workflows?

- How do you develop model customization and training methods that let customers get state-of-the-art results from foundation models?

- How do you adapt and extend protein and antibody models so customers can fine-tune on proprietary sequence data and get therapeutically relevant outputs?

You will work across drug discovery (protein engineering, antibody design) and clinical trial operations (agentic automation, structured reasoning, domain adaptation). You will own end-to-end scientific solutions from research through production, and your work will directly shape the tools that thousands of scientists use daily.

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

BASIC QUALIFICATIONS

- 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

PREFERRED QUALIFICATIONS

- 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

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 198,900.00 - 269,000.00 USD annually