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# ML Engineer, Manipulation

- **Company:** Diligent Robotics
- **Location:** Austin, TX, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Profiling, Python (Programming Language), Regression Testing, Software Engineering, Pytorch, Mobile Robots, Delivery Pipeline, AI Platforms, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0dd06c982db93f26

## About the Role

Do you have experience in Software development?, Do you have a Master's degree?, * 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.

## Description

As an ML Engineer, 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. You'll build perception-to-action models, training datasets, evaluation tooling, and deployment pipelines that improve robustness, generalization, and safety for real-world manipulation tasks at scale. Your work will directly impact the robot's ability to perform complex interactions consistently across real sites with minimal special-case engineering., * 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.


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