ML Engineer, Manipulation

Build and deploy machine learning systems that let mobile robots manipulate objects reliably in changing, human-centered environments. The work spans sensor-driven models, training data and evaluation, simulation-to-real transfer, and deployment on robot hardware. You will use field results to improve reliability, safety, and performance across real-world tasks.

• Develop learning-based models for robot interaction, including reaching, motion generation, and action execution.
• Create training pipelines using robot logs or teleoperation data, including dataset preparation, action representations, augmentation, and distributed training.
• Define evaluation metrics and regression checks for reliability, recovery behavior, and safety.
• Build simulation-to-real workflows with domain randomization and failure-mode testing.
• Optimize and distill models for edge hardware, measuring latency, memory use, and stability.
• Integrate learned policies with control and safety systems, then validate performance on robots.
• Analyze field failures and improve models through targeted data collection and retraining.

• Bachelor’s or master’s degree in robotics, computer science, electrical engineering, or a related field.
• At least three years applying machine learning to robotic manipulation, visuomotor control, or sequence models.
• Strong PyTorch skills and experience building dependable training and evaluation pipelines.
• Strong Python software engineering skills and ability to work across ML and robotics teams.

• Experience with vision-language-action models, behavior cloning, or transformer and diffusion policies for robotic control.
• Simulation-to-real manipulation training experience, including synthetic data and domain randomization.
• Edge deployment experience, such as ONNX or TensorRT, quantization, or performance profiling.
• Familiarity with safety-critical robotics and fallback or recovery behaviors.