> Markdown version of [/jobs/ext/1315607-telecommute-ml-engineer-manipulation](https://www.wearedevelopers.com/jobs/ext/1315607-telecommute-ml-engineer-manipulation). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# TELECOMMUTE ML Engineer, Manipulation

- **Company:** Diligent Robotics
- **Location:** 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:** July 17, 2026
- **Apply:** https://www.dice.com/job-detail/e85466b8-0eb5-4b56-b674-c4f52eb9e2bc

## About the Role

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., * 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

* 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.


## Related Videos

- [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated)
- [Robots are coming into the wild! Full-Stack Robotics Engineers, be ready!](https://www.wearedevelopers.com/videos/479-robots-are-coming-into-the-wild-full-stack-robotics-engineers-be-ready)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [Profiling Symfony & PHP apps with Blackfire](https://www.wearedevelopers.com/videos/265-profiling-symfony-php-apps-with-blackfire)
- [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms)
- [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [Prompt Engineering is a Job of the Past](https://www.wearedevelopers.com/magazine/342-prompt-engineering-is-a-job-of-the-past)
- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)