Inference Infrastructure Engineer

Highlights

• Own the infrastructure connecting foundation models to real robot behavior.
• Build deployment systems for generalist robots operating in real-world environments.
• Scale infrastructure as model complexity and deployment footprint grow.

What they offer

• Offers Equity

Ideal candidate

An infrastructure-focused engineer with 3+ years in ML systems who thrives on ownership and debugging complex distributed issues. You care about efficient, reliable deployment and seeing your work drive real-world robot behavior.

Requirements

• 3+ years of experience in ML infrastructure, MLOps, or distributed systems
• Strong proficiency with Kubernetes and containerized deployment pipelines
• Experience with GPU orchestration and resource scheduling across large distributed jobs
• Experience with cloud providers (e.g., AWS, GCP) and hybrid cloud/on-prem infrastructure
• Familiarity with ML frameworks (e.g., PyTorch, JAX) and model serving tools (e.g., Triton, Ray Serve, TorchServe)
• Strong debugging instincts and ownership mentality — comfortable driving issues to resolution across the stack