Founding Research Engineer, Robot Learning

True Talent Solutions Partners; San Francisco (CA); Hybride; USD 260 000 - 300 000; Plein temps

Avantages offerts par ce poste

Hybrid work in SF

Competitive salary

Visa sponsorship not available

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Robot learning

Python

PyTorch

Research in ML/robotics

Industry & academia experience

Formation

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

Outils

NVIDIA Isaac Sim

MuJoCo

Isaac Lab

Description du poste

About the Role

A physical AI data infrastructure company is seeking its first robot learning hire to test how synthetic training data affects robot policy performance. You will work with research, engineering, and product teams to turn rigorous experiments into public results and practical data requirements.

What You’ll Do

• Train robot policies using synthetic data, including VLA fine-tuning, imitation learning, diffusion policies, or reinforcement learning in simulation.

• Establish baselines on public evaluation suites and compare results using controlled experiments with consistent seeds and protocols.

• Assess manipulation and grasping performance, reporting improvements and failures clearly.

• Translate experimental findings into data specifications covering generation needs, fidelity, and variation.

• Publish and present results to the robotics community and participate in customer technical discussions.

• Help build and lead a robotics team as it grows.

What We’re Looking For

• At least 6 years of hands-on robot learning experience, including work in industry and university research environments.

• A master’s or doctoral degree in robotics, machine learning, or a related field.

• Experience training and evaluating robot policies in simulation with tools such as NVIDIA Isaac Sim or Isaac Lab, MuJoCo, or equivalent.

• Experience with methods such as VLA fine-tuning, imitation learning, diffusion policy, ACT, behavior cloning, or reinforcement learning.

• Background in robotics research or simulation products, with experience training manipulation or grasping policies.

• Ability to use evaluation results to guide data or simulation changes and run controlled comparisons that vary training data while keeping other factors consistent.

• Strong Python and PyTorch skills; familiarity with USD and sim-to-real transfer is relevant.

Compensation & Benefits

Base salary: $260,000 to $300,000 annually. Visa sponsorship is not available.

Location

Hybrid role based in San Francisco, United States.