Sr. Applied Scientist, AWS Applied AI Solutions - Life Sciences

About the role

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. The Life Sciences Applied AI Solutions team works alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. The team applies biological foundation models, large language models, and agentic reasoning systems to life sciences problems, delivering them as applications and managed services that customers can fine-tune, tailor, and deploy on their own data. Current focus areas include clinical trials (automating and optimizing regulatory and clinical development workflows) and drug design (products such as Amazon Bio Discovery that accelerate discovery with AI-guided protein engineering and antibody design capabilities). The team values scientific rigor, encourages publication, and supports conference participation.

We are seeking an Applied Scientist to build the models and methods behind our life sciences AI products with a primary focus on clinical trial operations and agentic reasoning. This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will design, train, and evaluate systems that reason over complex clinical and operational logic and ship them into products customers use directly. You will work closely with senior and principal scientists on well-scoped research problems, own your results end to end, and see your work reach production. The problems span building LLM-based agentic systems that correctly reason over clinical protocols, regulatory standards, and complex multi-step operational workflows; evaluating agent reliability and faithfulness rigorously enough for high-stakes clinical settings; and developing model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that let customers get strong results from foundation models on their own data. You will focus on clinical trial operations (agentic automation, structured reasoning, evaluation, domain adaptation) with opportunities to contribute across drug discovery (protein engineering, antibody design) as the portfolio grows.

Responsibilities

• Design, train, fine-tune, and evaluate LLM-based agentic systems that reason over clinical protocols, regulatory standards, and operational workflows
• Build rigorous evaluation harnesses and benchmarks to measure agent reliability, faithfulness, and failure modes in high-stakes domains
• Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that help customers get better outputs on their own data with less effort
• Contribute to graph-based and causal modeling approaches for clinical trial operations
• Partner with Life Sciences domain experts, product, and engineering to translate scientific challenges into shipped capabilities
• Own experiments end to end: problem framing, implementation, evaluation, iteration, and hand-off to production
• Publish at top-tier venues where the work supports it
• Contribute to drug discovery efforts (protein engineering, antibody design) as opportunities arise

• Design and run an experiment to validate a new agentic reasoning or fine-tuning method, then ship it as a capability customers can use
• Diagnose why a model is failing on a new class of inputs and implement a fix to unblock a delivery milestone
• Build or extend an evaluation benchmark to measure how faithfully an agent reasons over clinical logic
• Meet with domain experts to scope what the next model release needs to do
• Review results with a senior scientist, sharpen the approach, and get it over the finish line
• Prototype a new idea that could become the next capability in the product

Requirements

• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python, or related language

Preferred qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, TensorFlow, NumPy, SciPy, etc.

Benefits

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 at https://amazon.jobs/en/benefits.

Pay

Base salary range for this position in Seattle, WA: $167,100 – $226,100 USD annually. The Amazon package includes sign-on payments and restricted stock units (RSUs). Final compensation determined based on experience, qualifications, and location.

Schedule

Flexible work hours and arrangements are part of the culture; achieving success at work should never come at the expense of sacrifices at home.