Overview As a Principal Applied Scientist in AWS Life Sciences Applied AI Solutions, you will set the scientific direction for LLM reasoning and biological model customization. You'll design novel approaches and deliver models that empower scientists in drug design and clinical trials. You'll translate research into shipped capabilities at scale, collaborating with domain experts and customers. This role offers the chance to publish, mentor, and shape multi-year research roadmaps while impacting real-world life sciences workflows.
Compensation / Benefits- health insurance (medical, dental, vision)
- 401(k) matching
- paid time off
- RSUs / sign-on incentives
- parental leave
- reimbursement for adoption and surrogacy
Responsibilities- Define the scientific vision for LLM reasoning and agentic AI across the portfolio
- Advance reasoning, planning, and agentic methods for complex scientific workflows
- Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) for customer data
- Extend biological foundation models for customer-specific therapeutic use-cases
- Solve open problems in faithful reasoning, multi-step planning, and tool use in high-stakes domains
- Collaborate with Life Sciences domain experts and customers to translate challenges into research problems
- Publish results at top venues and build external scientific reputation
- Mentor applied scientists while maintaining strong personal research contributions
- Work with product and engineering to translate research into shipped products at scale
- Influence multi-year research roadmaps through deep expertise and customer understanding
Key requirements- PhD in ML, CS, Computational Biology, or related field
- Senior or principal-level applied scientist with track record of solving complex technical problems
- Deep expertise in LLMs, reasoning systems, and agentic AI (architecture, training, fine-tuning, optimization)
- Strong publication record at top-tier ML or computational biology venues
- Experience innovating on model customization (fine-tuning, RLHF, retrieval augmentation, domain adaptation) for downstream apps
- Domain experience in life sciences or related scientific fields (protein engineering, antibody design, clinical data, or regulatory science)
- Experience translating research into products or services used by others
- Experience delivering 0-to-1 AI products from research to first customer delivery
- Excellent Python programming and deep learning framework skills (PyTorch, JAX)
- collaboration
- mentoring
- communication of complex ideas
- LLMs
- reasoning systems
- agentic AI
