AI Data & Simulation Product Manager (SF Onsite)

Success Matcher Recruitment; San Francisco (CA); On-site; USD 150,000 - 240,000; Full time

Benefits offered by this job

Health, dental, and vision coverage

Equity

In-office in San Francisco

Job summary

Success Matcher Recruitment is representing a seed-stage AI data research lab in San Francisco seeking a Research Product Manager. You will co-design model capabilities with researchers, address long-horizon performance gaps, and drive experimental benchmarks using internal stacks and open-source tools.

The role combines product leadership with hands-on ML experimentation, requiring a CS degree or ML background, and experience running ML experiments.

Qualifications

• Hands-on experience with RL training data, synthetic data generation, or simulation environments.
• Ability to diagnose model failure modes and build targeted training data solutions.
• Strong CS/ML fundamentals with ability to read code and understand model architecture.
• Experience running ML experiments and interpreting benchmarking data.
• High drive and adaptability for an early-stage, fast-moving meritocracy.

Responsibilities

• Co-design model capabilities with researchers and diagnose performance gaps.
• Architect synthetic data pipelines and simulation environments using our client's engine to address model weaknesses.
• Execute experiments and benchmarks using internal simulation stacks and open-source tooling.
• Engage directly with researchers at AI labs and Fortune 500 enterprises to identify where state-of-the-art models fail in real-world scenarios.
• Influence the product roadmap, go-to-market strategy, and overall research direction based on evaluation metrics.

Skills

RL training data

Synthetic data generation

Model evaluation/benchmarking

Code literacy

Fast-moving startup mindset

Education

CS degree / ML background

Job description

Success Matcher Recruitment is representing a seed-stage AI data research lab in San Francisco seeking a Research Product Manager. You will co-design model capabilities with researchers, address long-horizon performance gaps, and drive experimental benchmarks using internal stacks and open-source tools.

The role combines product leadership with hands-on ML experimentation, requiring a CS degree or ML background, and experience running ML experiments.