Diligent RoboticsRemote
ML Engineer II, Manipulation
United States
Full Time
23 hours ago
H1B Sponsor Likely
Key skills
ML in robotics manipulationPyTorchPythonVision-Language-Action modelssim-to-real trainingdeploying ML modelssoftware engineering skillssafety-critical roboticscollaboration skillsAIML
About this role
Diligent Robotics envisions a future powered by robots that work seamlessly with human teams. As an ML Engineer II (Manipulation), you will develop and deploy learning-based manipulation systems that enable mobile robots to interact reliably with the physical world in dynamic human environments.
Responsibilities:
- Develop learning-based manipulation models for end to end sensor-driven interaction (e.g., reaching, motion generation, and execution in dynamic environments)
- Build and maintain manipulation training pipelines: dataset creation from robot logs/teleop, action representations, augmentation, and distributed training
- Design evaluation metrics and regression tests that quantify manipulation reliability, recovery behavior, and safety in real environments
- Develop sim-to-real workflows for manipulation learning, including simulation environments, domain randomization, and failure-mode testing
- Optimize and distill models for edge deployment; benchmark latency, memory use, and stability on target hardware
- Partner with the AI platform team to integrate policies with control and safety systems, and validate end-to-end performance on robots
- Analyze field performance, identify dominant failure modes, and drive iterative improvements through data collection and targeted retraining
Requirements:
- Bachelor's or Master's degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus)
- 3+ years of experience applying ML to robotics manipulation, visuomotor control, or sequential to sequence models
- Strong proficiency in PyTorch and experience building reliable training/evaluation pipelines
- Strong software engineering skills in Python; ability to collaborate across ML and robotics teams
- Experience with Vision-Language-Action (VLA) models, behavior cloning, and/or transformer/diffusion policies for robotic control
- Experience with sim-to-real training for manipulation (Isaac Sim/Mujoco or similar), including domain randomization and synthetic data
- Experience deploying ML models to edge hardware (ONNX/TensorRT, quantization, performance profiling)
- Familiarity with safety-critical robotics integration and designing fallback/recovery behaviors