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 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 clinical trials, we're building AI that automates and optimizes regulatory and clinical development workflows. In drug design, our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. 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 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. 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.
This role combines expertise in LLM reasoning and agentic AI with applied impact in life sciences. You will work on how large language models reason, plan, and act in complex scientific domains, while applying domain knowledge 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 operational workflows?
- How do you evaluate agent reliability and faithfulness rigorously enough to trust in high-stakes clinical settings?
- How do you develop model customization methods (fine-tuning, 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. You will own end-to-end scientific solutions from research through production, and your work will directly shape the tools that scientists use daily.
Key job 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
A day in the life
- 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
BASIC QUALIFICATIONS
- Master's degree or above in a relevant field
- Applied research experience with a track record of solving complex technical problems and delivering results
- Experience with LLMs, reasoning systems, and agentic AI, including architecture design, training, fine-tuning, and evaluation
- Experience building or evaluating agentic systems (planning, tool use, retrieval, verification)
- Publication record at ML or computational biology venues
- PhD in Machine Learning, Computer Science, Computational Biology, or related field, or MS with equivalent applied research experience
- Demonstrated ability to apply model customization techniques (fine-tuning, RLHF, retrieval augmentation, domain adaptation) to specific downstream applications
- 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 designing agent evaluations or benchmarks for scientific or high-stakes domains
- Experience with clinical data standards (e.g., SDTM/ADaM) or regulatory science
- Experience with graph neural networks or causal inference
- Domain experience in life sciences or computational biology (protein engineering, antibody design, genomics, or clinical data)
- Experience translating research into products or services that others use
- Experience taking 0-to-1 capabilities from initial research through first customer delivery
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 - 142,800.00 - 193,200.00 USD annually
