Pioneering Robot Learning Research Engineer

True Talent Solutions Partners; San Francisco (CA); Hybrid; USD 260,000 - 300,000; Full time

Benefits offered by this job

Hybrid work in SF

Competitive salary

Visa sponsorship not available

Job summary

True Talent Solutions Partners in San Francisco is seeking its first robot learning hire to advance synthetic data experiments for robot policy performance. You will collaborate with research, engineering, and product teams to turn rigorous experiments into public results and practical data requirements.

You will train robot policies using synthetic data, establish baselines on evaluation suites, and translate findings into data specifications for generation needs and variation.

Qualifications

• At least 6 years of hands-on robot learning experience in industry and university settings.
• Master’s or doctoral degree in robotics, machine learning, or related field.
• Experience training and evaluating robot policies in simulation with tools like Isaac Sim or MuJoCo.
• Strong Python and PyTorch skills; familiarity with USD and sim-to-real transfer.

Responsibilities

• Train robot policies using synthetic data, including VLA fine-tuning, imitation learning, diffusion policies, or reinforcement learning.
• Establish baselines on public evaluation suites and run controlled experiments with consistent seeds.
• Assess manipulation and grasping performance, reporting improvements and failures clearly.
• Translate findings into data specifications for generation needs, fidelity, and variation.
• Publish results to the robotics community and participate in customer technical discussions.
• Help build and lead a robotics team as it grows.

Skills

Robot learning

Python

PyTorch

Research in ML/robotics

Industry & academia experience

Education

Master's or PhD in robotics, ML, or related field

Tools

NVIDIA Isaac Sim

MuJoCo

Isaac Lab

Job description

True Talent Solutions Partners in San Francisco is seeking its first robot learning hire to advance synthetic data experiments for robot policy performance. You will collaborate with research, engineering, and product teams to turn rigorous experiments into public results and practical data requirements.

You will train robot policies using synthetic data, establish baselines on evaluation suites, and translate findings into data specifications for generation needs and variation.