Note: The job is a remote job and is open to candidates in USA. Diligent Robotics is building artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. As an ML Engineer, Manipulation, you will develop and deploy learning-based manipulation systems for mobile robots, focusing on enhancing their ability to interact reliably with the physical world. Your role will involve creating perception-to-action models and building evaluation tools that improve the robustness and safety of robotic interactions.
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
Skills
- 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
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