We are seeking a Principal Applied Scientist to own the scientific vision across Agentic WorkSpaces. This is a foundational role spanning the full portfolio - Personal, Applications, and Core, and the agentic surfaces (WS4Builders and WorkSpaces for Agents). You will define how we measure, improve, and guarantee the performance of AI agents and human-AI teams. A core part of the role is defining the science agenda itself - identifying which problems are most worth solving and where the highest-leverage bets lie. Directions worth exploring might include Organizational Intelligence (turning institutional knowledge into agent-consumable skills), AI Agent Experience / AiAX (agent observability and autonomous remediation), and contextual, behavioral security that adapts enforcement in real time for human and agent sessions - but these are illustrative examples, not a fixed roadmap, and many other directions are possible. You will help define which ones we pursue. The problems you will solve do not have established industry patterns. You will set the direction for the science of how AI agents and people perceive, reason about, and act reliably within computing environments at enterprise scale.
Key job responsibilities
- Set the long-term scientific vision: Define what best-in-class agent performance, evaluation, and learning look like across Agentic WorkSpaces - for computer-using agents and human-AI teams alike. Identify the unsolved scientific problems, chart a multi-year research roadmap, and secure buy-in from VP-level leadership.
- Solve highly ambiguous, novel problems: Independently frame and deliver solutions to foundational challenges in agent perception, reasoning, evaluation, reliability, and human-AI collaboration - problems where neither the approach nor the success criteria are pre-defined.
- Own the evaluation and measurement foundation: Build the benchmarks, datasets, and metrics that quantify agent and team accuracy, cost, productivity, and safety across the portfolio and diverse enterprise workflows, and that gate what we ship.
- Drive cross-organizational scientific alignment: Work across partner teams (AgentCore, Bedrock model teams, Identity, Security, the MCP ecosystem) and across the Applied AI Solutions product portfolio to shape how models and agent frameworks are applied, and ensure scientific decisions compose into a coherent system.
- Deliver measurable business impact: Ensure research translates to customer outcomes: higher task accuracy, lower cost-per-action, faster time-to-production, measurable productivity for human-AI teams, and the trust that lets enterprises scale agent workflows.
- Raise the scientific bar: Establish rigor in experimentation, evaluation, and reproducibility. Mentor and grow senior scientists and engineers. Set the standard for applied science quality across the organization.
- Advance the state of the art: Contribute to the external technical community through publications, patents, and open-source contributions that position AWS as the leader in the science of secure agent-computer interaction and human-AI teamwork.
BASIC QUALIFICATIONS
- PhD in Computer Science, Machine Learning, or a related field, or a Master's with equivalent applied research experience
- 10+ years of applied science experience, including 8+ years applying ML to real-world products at scale
- Experience setting research direction and technical strategy across multiple teams and organizations
- Deep expertise in modern ML, including LLMs / foundation models, evaluation methodology, and experimental design
- Track record of delivering complex, ambiguous research initiatives from concept through production in enterprise environments
PREFERRED QUALIFICATIONS
- Research or applied experience with AI agents, tool use, computer-use / GUI-grounded agents, or autonomous systems
- Experience designing benchmarks, evaluation harnesses, and metrics for non-deterministic or agentic systems
- Experience with reinforcement learning, imitation / behavior learning, or learning from demonstration
- Experience with agent safety, grounding, guardrails, or reliability for LLM-based systems
- Familiarity with enterprise constraints: security, auditability, and compliance frameworks (NIST, SOC2, FedRAMP, HIPAA)
- Publications, patents, or significant open-source contributions in ML, agents, or AI infrastructure
- Experience influencing technical direction at VP+ level in a large technology organization
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, CA, Sunnyvale - 228,700.00 - 309,400.00 USD annually
USA, NY, New York - 218,800.00 - 295,900.00 USD annually
USA, WA, Seattle - 198,900.00 - 269,000.00 USD annually
