Salary: upto $250,000
Location: San Francisco, CA
Work on advanced AI-driven physical systems with broad manipulation and experimental capability. I’m seeking a Senior Digital Twin ML Engineer to build high-fidelity digital twins of robotic, electromechanical, and experimental platforms.
You will design model-identification pipelines, calibration routines, dynamic-model learning systems, and multi-scale physics representations that support accurate predictive simulation and closed-loop interaction with RL, planning, and control stacks. This role blends physics intuition, ML modeling, and hands-on experimentation to ensure digital twins remain stable, accurate, and continuously updated as real systems evolve.
Responsibilities:
- Build model-identification and parameter-estimation pipelines with adaptive calibration.
- Develop ML-based dynamic models, multi-scale physics approximators, and hybrid simulation frameworks.
- Maintain twin fidelity, stability, and version consistency as data and hardware change.
- Work closely with simulation, RL, controls, and agent teams to integrate twins into decision-making and learning workflows.
Qualifications:
- Strong experience creating or calibrating digital twins or dynamic, data-driven physics models.
- Knowledge of system identification, time-series modeling, and physical parameter estimation.
- Ability to combine physics, ML, and experimental data into robust predictive models.
- Comfort operating across ML, simulation tooling, and physical hardware interfaces in a fast-paced environment.
