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Machine Learning Engineer — Robotics (Hugging Face + Isaac Sim)
Girder AI
Location
🇺🇸 San Francisco, United States
Type
full_time
Salary
Undisclosed
Posted
4d ago
Job Description
About the Role
We're looking for a Machine Learning Engineer to train, fine-tune, and deploy learned models for our robotics stack. You'll work at the intersection of modern foundation models and physical systems — pulling models and datasets from the Hugging Face ecosystem, fine-tuning them for our domain, and validating them in NVIDIA Isaac Sim before deploying to real hardware. This role is ideal for someone who is fluent in the modern ML tooling stack (Transformers, PEFT, LeRobot, Datasets) and wants their models to move real machines, not just produce benchmark numbers.
What You'll Do
- Fine-tune and adapt foundation models — vision-language models, vision-language-action (VLA) policies, and perception models — using the Hugging Face ecosystem (Transformers, PEFT/LoRA, Accelerate, Datasets)
- Build training pipelines for imitation learning and robot policy learning, including data collection, curation, and versioning (e.g., LeRobot-style datasets)
- Generate and leverage synthetic training data from Isaac Sim, including domain randomization for sim-to-real transfer
- Evaluate policies and perception models in Isaac Sim / Isaac Lab environments: define metrics, build eval harnesses, and run closed-loop rollouts
- Optimize models for deployment on edge GPUs (quantization, distillation, TensorRT/ONNX export)
- Track, analyze, and iterate on experiments with tools like Weights & Biases or MLflow
- Collaborate with simulation and robotics engineers to close the loop between data generation, training, sim evaluation, and hardware deployment