Senior ML Engineer, Fauna
About The Position
We are seeking a Machine Learning Engineer to work directly alongside our research scientists to train, evaluate, and deploy the models that make our robots move, perceive, and act in the real world. This is a hands-on ML role: you will train policies, debug convergence, run experiments in simulation, and push models onto hardware — not just build the pipes around them. You’ll bring deep expertise in reinforcement learning, computer vision, and supervised learning applied to robotics and embodied systems. You also need to think seriously about training infrastructure — managing GPU clusters, optimizing distributed training, and shipping models to edge devices — but the core of this role is getting in the loop with scientists and making models work.
Requirements
- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- 5+ years of non-internship professional experience in applied ML engineering with direct model training responsibilities
- Master’s or PhD in Computer Science, Machine Learning, Robotics, or related field, or equivalent practical experience
- Strong software engineering fundamentals in Python
- Deep proficiency with machine learning toolkits (PyTorch, etc.) with experience designing, training, and debugging non-trivial model architectures
- Track record of collaboration with research scientists in fast-paced environments
Nice To Haves
- Hands-on experience with reinforcement learning or imitation learning
- Proficiency with containerization (Docker) and orchestration (Kubernetes).
- Experience with model optimization for edge deployment (quantization, pruning, TensorRT, ONNX)
- Experience with physics simulation environments (Isaac Lab, MuJoCo, PyBullet, or equivalent)
- Experience with ML infrastructure tools (e.g., MLflow, Weights & Biases, Kubeflow, Ray).
Responsibilities
- Train and iterate on neural network policies for locomotion, manipulation, navigation, and perception using reinforcement and supervised learning
- Design and run experiments in simulation (Isaac Lab, MuJoCo, or similar) and transfer results to physical hardware
- Debug training runs end-to-end: diagnosing convergence failures, reward shaping issues, data quality problems, and sim-to-real gaps
- Optimize models for deployment on edge hardware (NVIDIA Jetson) with strict latency and memory constraints
- Build and maintain MLOps infrastructure: experiment tracking, model versioning, evaluation pipelines, and reproducible training workflows
Benefits
- sign-on payments
- restricted stock units (RSUs)
- health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
- 401(k) matching
- paid time off
- parental leave
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What This Job Offers
Job Type
Full-time
Career Level
Senior
