Torc Robotics
ML Engineer, II - Simulation Enablement
About this role
Join Torc's Dataloop + Simulation division as an ML Engineer II to bridge simulation platform development and autonomous vehicle model teams. You'll embed with Perception or Behavior model teams to drive adoption of Torc Sim, enabling replay, recomputation, and evaluation of autonomy models at scale while becoming a fluent translator between simulation infrastructure and model development.
What you'll do
- Serve as embedded liaison between Dataloop + Simulation and one to two Autonomy teams to drive Torc Sim adoption
- Implement end-to-end data flows from data ops platforms through simulation environments to persistent storage at scale
- Onboard Autonomy models to execute replay and recompute workflows, plus subsequent metric evaluation
- Ensure model replay/recompute jobs run reliably at scale with interpretable and auditable metrics
- Build visualization and debugging tools to help engineers understand model behavior and simulation results
- Become domain expert on assigned Perception or Behavior models to fluently translate between platform and model needs
What they're looking for
- Machine learning systems and model evaluation
- Data pipeline engineering and distributed systems
- Autonomy/autonomous vehicle fundamentals
- Python and software engineering best practices
- Metrics design and interpretability
- Visualization and debugging tools
- Cloud infrastructure and scalability
- Cross-functional collaboration and communication
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Torc Robotics
Torc Robotics develops autonomous driving software and systems for trucks. The company is hiring systems engineers, software engineers, and test automation specialists to build validation frameworks, integration environments, and web applications that ensure the reliability and quality of their autonomous vehicle technology.
- Website
- torcrobotics.com
Likely interview questions
- What experience do you have working with machine learning models in production, and how have you debugged model issues at scale?
- Describe a time you built a data pipeline that connected multiple systems—how did you ensure data quality and auditability?