Robotics ML Engineer: Data-to-Deployment on Real Robots (SF)

Jack; San Francisco (CA); On-site; USD 150,000 - 250,000; Full time

Benefits offered by this job

Equity 0.1%-1%

Relocation assistance

Job summary

Jack in San Francisco is hiring a robotics machine learning engineer to own robot learning end-to-end, from data to deployed policy, in a venture-backed startup that builds its own hardware and models. You will lead data pipelines, model post-training, and hardware calibration for real robots.

The role emphasizes applied ML on real robots with a base salary of $150k–$250k and equity 0.1%–1%, on-site five days a week in San Francisco (Potrero Hill), with relocation available.

Qualifications

• 0 to 7 years of applied machine learning engineering experience.
• Hands-on work with physical robotic systems (robots, manipulators, drones or autonomous vehicles)
• You've trained and deployed a learned policy or ML model on real hardware
• Strong Python, plus PyTorch, JAX or an equivalent framework
• Bonus: C++ or Rust, post-training policies, robot data collection

Responsibilities

• Build pipelines that ingest and quality-check terabytes of robot data a day
• Post-train models and set up fast, reliable training pipelines
• Calibrate hardware and run data QC so models perform well on real robots
• Set the company's standards for data processing and model training
• Keep up with new robot learning research and apply what works

Skills

Python

PyTorch

JAX

C++/Rust

Job description

Jack in San Francisco is hiring a robotics machine learning engineer to own robot learning end-to-end, from data to deployed policy, in a venture-backed startup that builds its own hardware and models. You will lead data pipelines, model post-training, and hardware calibration for real robots.

The role emphasizes applied ML on real robots with a base salary of $150k–$250k and equity 0.1%–1%, on-site five days a week in San Francisco (Potrero Hill), with relocation available.