Overview As Senior Manager, Applied Science, you will build and lead a team of applied scientists across the Agentic WorkSpaces portfolio to measure and improve AI agents and human-AI collaboration at enterprise scale. You'll set the science direction, define high leverage research, and translate findings into production systems with cross functional partners. You'll partner with executives and customers to articulate impact, safety, and productivity gains, shaping a long term scientific vision for AAWS.
Compensation / Benefits- health insurance (medical, dental, vision)
- 401(k) matching
- RSUs
- paid time off
- parential leave
- sign-on payments
Responsibilities- Build and lead the applied science team across the AAWS portfolio; recruit, develop, and retain top scientific talent
- Own the science strategy: define benchmarks, task suites, and metrics to rigorously measure agent and human-AI performance
- Define high leverage research directions and shape the science agenda (e.g., tools use, visual reasoning, organizational intelligence, AiAX)
- Translate science into shipped product; collaborate with engineering, product, and program leaders to scale models and learning systems
- Represent science in leadership and with enterprise customers on performance, safety, and productivity metrics
- Set and communicate a long term scientific vision and multi year roadmap; secure VP level buy in
- Mentor scientists toward senior levels; raise the scientific bar across the organization
- Drive cross organizational alignment (with AgentCore, Bedrock model teams, Identity, Security, MCP ecosystem)
- Deliver measurable business impact: higher task accuracy, lower cost per action, faster time to production, improved productivity
- Establish rigorous experimentation, evaluation, and reproducibility standards; enable contributions to external communities
- Advance state of the art through publications, patents, and open source work
Key requirements- PhD or Master's in Computer Science, Machine Learning, or a related field, or equivalent applied research experience
- 10+ years of applied science experience, including 3+ years in people leadership
- Experience setting research direction across multiple teams and organizations
- Deep expertise in modern ML, including LLMs/foundation models and evaluation methodologies
- Track record delivering complex, ambiguous research initiatives from concept to production in enterprise environments
- Preferred: leading science teams on AI agents, tool use, GUI grounded or autonomous systems, and building early stage teams
- Experience designing benchmarks, evaluation harnesses, and metrics for non deterministic or agentic systems
- Familiarity with agent safety, grounding, guardrails, reliability for LLMs, and enterprise constraints (security, auditability, compliance)
- Publications, patents, or open source contributions demonstrating scientific leadership
- Ability to influence technical direction at VP+ level in a large technology organization
- Strategic thinking and vision
- Cross functional collaboration
- People leadership and mentorship
- Modern ML and LLMs/foundation models
- Evaluation methodology and metrics design
- Benchmarks and evaluation harnesses for non deterministic/agentic systems
