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Senior Machine Learning Engineer - End-to-End

Torc Robotics
Location
Ann Arbor, MI (U.S.) · Remote in the United States
Workplace
Remote
Employment
Full Time
Salary
USD 226,400–271,700/yr
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Posted 22d ago

The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.

Responsibilities

  • Own development and delivery of End-to-End ML models
  • Train and evaluate models using large-scale datasets
  • Analyze model performance, identify failure modes, and drive improvements
  • Design and refine training pipelines, data workflows, and evaluation strategies
  • Contribute to model architecture decisions
  • Collaborate with Perception, Prediction, Planning, and Simulation teams
  • Support integration of E2E models into simulation and on-vehicle systems
  • Improve tooling, experimentation workflows, and reproducibility
  • Mentor junior engineers and contribute to team-level best practices

Requirements

  • Bachelor’s degree with 6+ years, Master’s with 4+ years, or PhD with 0–2 years of experience in Machine Learning, Robotics, Computer Science, or a related field
  • Track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, CoRL)
  • Experience developing and deploying ML models for autonomous systems, robotics, or complex decision-making environments
  • Strong programming skills in Python and PyTorch
  • Ability to write production-quality ML code
  • Experience training and evaluating models using large-scale datasets and distributed compute environments
  • Solid understanding of ML architectures used in E2E systems, such as Transformers, BEV models, VLA/VLM approaches, or diffusion models
  • Proven ability to debug model behavior, analyze performance metrics, and drive iterative improvements
  • Experience contributing to or influencing model architecture and training strategies
  • Ability to work cross-functionally and integrate ML systems into larger autonomy pipelines

Preferred

  • Experience developing End-to-End or mid-to-end models for autonomous driving or robotics
  • Experience with vision-language models (VLMs) or vision-language-action (VLA) systems
  • Familiarity with closed-loop simulation and evaluation frameworks
  • Experience with reinforcement learning or imitation learning in real-world systems
  • Experience with distributed training frameworks (e.g., Ray)
  • Understanding of vehicle dynamics, motion planning, or multi-agent systems

Skills

  • Python
  • PyTorch
  • Transformers
  • BEV models
  • VLA models
  • VLM models
  • Diffusion models
  • Ray

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