Applied Scientist, AWS Applied AI Solutions

About the Role

AWS Applied AI Solutions (AAIS) is building AI solutions that improve human capabilities and transform entire business functions. The team tackles open problems at the intersection of frontier AI and life sciences, applying biological foundation models, large language models, and agentic reasoning systems to life sciences problems.

The Role

Applied Scientist to build the models and methods behind life sciences AI products, with a primary focus on clinical trial operations and agentic reasoning. Design, train, and evaluate systems that reason over complex clinical and operational logic and ship them into products customers use directly. Work closely with senior and principal scientists, own results end-to-end, and see work reach production.

Key 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.

Requirements & Tech Stack

• 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.

Qualifications (Preferred)

• 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.