Lead Scientist, Agentic AI for Autonomous IT Troubleshooting

Amazon Web Services (AWS); Seattle (WA); On-site; USD 199,000 - 269,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Development Center U.S., Inc. in Seattle seeks a Principal Applied Scientist to own the scientific vision for WorkSpaces Advisor, an agentic AI system for troubleshooting enterprise workspaces.

You will set the roadmap to transform Advisor into an autonomous reasoning agent with multi-step remediation capabilities. You will drive research directions, design causal inference and RLHF/RAG learning systems, and build guardrails for safe, scalable deployment.

Qualifications

• PhD in a relevant technical field.
• 5+ years of hands-on predictive modeling experience.
• Experience distilling customer requirements into problem definitions in ambiguous settings.
• Experience mentoring junior scientists and engineers.
• Proficient in Java, C++, Python and related languages.

Responsibilities

• Define the scientific vision and long-term research agenda for agentic troubleshooting.
• Deliver breakthrough solutions on highly ambiguous problems in agentic AI for troubleshooting.
• Influence and align science with product, engineering, and business teams.
• Mentor scientists and engineers; establish best practices for experimentation and deployment.
• Own end-to-end production systems from research to deployment with measurable impact.
• Advance state of the art through external publications and engagements.

Skills

Predictive modeling

Java, C++, Python

Leading scientists

Ambiguity handling

Education

PhD in Electrical Engineering, Computer Science, Mathematics

Job description

Amazon Development Center U.S., Inc. in Seattle seeks a Principal Applied Scientist to own the scientific vision for WorkSpaces Advisor, an agentic AI system for troubleshooting enterprise workspaces.

You will set the roadmap to transform Advisor into an autonomous reasoning agent with multi-step remediation capabilities. You will drive research directions, design causal inference and RLHF/RAG learning systems, and build guardrails for safe, scalable deployment.