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Applied Scientist, AWS Applied AI Solutions - Life Sciences

Job in Seattle, King County, Washington, 98127, USA
Listing for: Amazon Science
Full Time position
Listed on 2026-10-12
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 143000 - 193000 USD Yearly USD 143000.00 193000.00 YEAR
Job Description & How to Apply Below

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 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.

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.

  • 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?
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…
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