Applied AI Engineer — On-site SF, Equity + Unlimited PTO

Raydar; San Francisco (CA); On-site; USD 200,000 - 275,000; Full time

Benefits offered by this job

Health, dental and vision

Life and disability insurance

401(k) with automatic enrollment

Unlimited paid time off

Parental leave

Mental health support

Fertility and family-building support

Equity

Job summary

Raydar is seeking a hands-on AI research engineer to build and evaluate AI agents that run in production, turning new research into reliable live-data systems.

The role blends engineering and experimentation, giving you ownership over how results are measured, validated and shipped to production. You will design evaluation frameworks, address model reliability and inference costs, and contribute to production infrastructure.

Qualifications

• Experience at high-growth technology companies or AI research labs.
• A practical record of building sophisticated automated software and networked computing systems.
• Background designing testing tools that measure how automated assistants perform.
• Proficiency in Python programming.
• A background from a top university.
• A clearly demonstrable track record, such as an Olympiad result, a major hackathon win, a widely used open-source project or published AI research.

Responsibilities

• Design and run applied research programs on model reliability, inference cost and output quality.
• Build evaluation frameworks that judge the full sequence of an agent's actions, not only its final output.
• Contribute to how automated assistants store, look up and use prior information.
• Convert research findings into production infrastructure with measurable impact.
• Help set the direction and priorities of the applied research work.

Skills

Python

Education

Top university background

Tools

Testing frameworks

Job description

Raydar is seeking a hands-on AI research engineer to build and evaluate AI agents that run in production, turning new research into reliable live-data systems.

The role blends engineering and experimentation, giving you ownership over how results are measured, validated and shipped to production. You will design evaluation frameworks, address model reliability and inference costs, and contribute to production infrastructure.