ML Infra Engineer, Modeling
Physical Intelligence · San Francisco
CHECKED 14H AGO
Roboticsonsite
At a glance
Highlights
- Own critical infrastructure for large-scale training of robotic foundation models.
- Turn research ideas into production training runs for physically-actuated devices.
- Make large-scale training reliable, reproducible, and fast at scale.
Ideal candidate
A hands-on infrastructure-minded engineer who loves enabling researchers and optimizing large-scale training. You take ownership, debug across the stack, and care about reliability and iteration speed for real-world robots.
Requirements
- Strong software engineering fundamentals and experience building ML training infrastructure or internal platforms.
- Hands-on large-scale training experience in JAX (preferred), PyTorch.
- Familiarity with distributed training, multi-host setups, data loaders, and evaluation pipelines.
- Experience managing training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS).
- Ability to debug and optimize performance bottlenecks across the training stack.
- Strong cross-functional communication and ownership mindset.
Lab profile
About Physical Intelligence
Developing general-purpose AI foundation models to power robots for any physical task.
What they build
- General-purpose robot foundation models
- Learning algorithms for robot control
- Robotic systems and platforms
Known for
- Foundation model and system-building research
- Large-scale multimodal robot data operations
- Real-world fleet deployment and operations
- Prototype-to-production hardware engineering