Machine Learning Engineer — Robotics (Hugging Face + Isaac Sim)

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 Stay current with the physical AI landscape (VLAs, world models, robot foundation models) and rapidly prototype promising approaches What We're Looking For Required: 3+ years of experience in machine learning engineering, with at least some of that applied to robotics, autonomy, or embodied AI Deep, hands-on experience with the Hugging Face ecosystem: Transformers, Datasets, PEFT, Accelerate, and the Hub workflow (pushing/pulling models, datasets, spaces) Strong PyTorch skills and solid understanding of transformer architectures, fine-tuning techniques, and training dynamics Working experience with NVIDIA Isaac Sim (and ideally Isaac Lab) — comfortable setting up scenes, running policies in sim, and using sim outputs for training or evaluation Strong Python engineering fundamentals: clean code, testing, reproducible environments Comfort with Linux, GPUs, and distributed or multi-GPU training Nice to Have: Experience with vision-language-action models (e.g., GR00T, OpenVLA, π0) or robot learning frameworks like LeRobot Experience with reinforcement learning in GPU-parallelized simulators (Isaac Lab, MuJoCo, etc.) Sim-to-real transfer experience: domain randomization, system identification, or real-robot deployment Familiarity with ROS 2 and real-time inference on embedded platforms (Jetson, etc.) Experience with synthetic data pipelines (Isaac Replicator, Cosmos, or similar) Contributions to open-source ML or robotics projects $70K/yr - $300K/yr