Senior Digital Twin ML Engineer

Senior Digital Twin ML Engineer | SF or Redwood City

We’re hiring a Senior Digital Twin ML Engineer to join a frontier Physical AI startup building systems with general physical capability — the ability to experiment, engineer, and manufacture anything end to end.

This is a small, world-class algorithmic team creating high-fidelity digital twins that allow agents and reinforcement learning systems to reason about, predict, and ultimately control real physical hardware inside a 20,000 sq ft physical intelligence factory.

The Role

This role owns the modeling and system-identification pipelines that translate messy, real-world dynamics into stable, continuously updated simulators.

Your work sits at the core of planning, control, and closed-loop experimentation — directly determining how quickly autonomous systems can learn in simulation and transfer that learning into reliable real-world behavior.

What You’ll Do

• Build system identification, calibration, and parameter-fitting pipelines for evolving physical systems
• Develop ML-driven dynamic models and hybrid physics-based simulators for accurate prediction and control
• Maintain continuously updated digital twins as hardware, processes, and environments change
• Integrate digital twins with RL, control, and agent stacks to accelerate real-world learning
• Partner closely with robotics, controls, and infrastructure teams on model fidelity and deployment

What We’re Looking For

• Strong background in system identification, dynamics, simulation, or model-based RL
• Experience building digital twins or simulators for real physical systems (robotics, manufacturing, hardware)
• Comfort combining first-principles physics with data-driven ML approaches
• Ability to work close to hardware and iterate in tight experiment–model–deploy loops
• Thrives in early-stage environments where foundational technical choices matter

Why This Role

• Own the digital representation of a real, large-scale physical factory
• Work on problems where simulation fidelity directly impacts real-world outcomes
• Collaborate with a top-tier team across robotics, ML, and physical systems
• Help define how physical AI systems learn, plan, and act in the real world

If this sounds interesting, apply now!