Machine Learning Engineer

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

We're hiring a robotics machine learning engineer at a venture-backed robotics startup in San Francisco that builds its own hardware and trains its own models to automate electronics manufacturing. The team just passed 100 hours of robot data and is scaling collection fast. This is applied work on real robots, not research for its own sake, and you'd own robot learning end to end, from data to deployed policy.

What you'll do

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

What we're looking for

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

Details

Base salary $150K to $250K plus 0.1% to 1% equity

On-site 5 days a week in San Francisco (Potrero Hill), with occasional Saturdays. Relocation available

Some of the work supports defense and government robotics applications