> Markdown version of [/jobs/ext/2714962-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2714962-machine-learning-engineer). 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). --- # Machine Learning Engineer - **Company:** Path Robotics - **Location:** United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Training Data, Computer Programming, Software Debugging, High-Level Architecture, Python (Programming Language), Machine Learning, Management of Software Versions, Reinforcement Learning, Pytorch, Delivery Pipeline, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Stable Diffusion, Data Pipelines - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/machine-learning-engineer-robot-learning-loco-manipulation-path-robotics-7951792 ## About the Role * Ph.D. or Master's degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field - or equivalent experience. * 2+ years of hands-on robot learning experience. You have trained policies and deployed them on real robot hardware - not just in simulation. * Sim-to-real transfer experience - built simulation environments, implemented domain randomisation, transferred policies to physical robots, debugged where it broke. * Implementation experience with diffusion-based or flow-matching action policies for robots, and with action chunking. * Reinforcement learning for robotics applied on real hardware - sample-efficient on-robot methods, residual RL on top of pretrained policies, on-policy fine-tuning of foundation policies. * Strong programming skills in Python; PyTorch and ML training infrastructure at production level. * Practical experience with NVIDIA Isaac Sim / Isaac Lab, MuJoCo, or equivalent. * Comfort with physical robots - debugging, iterating, deploying. * Strong communication skills, able to convey complex technical concepts to a diverse audience. Strongly Preferred: * Edge inference on edge-class hardware (TensorRT, ONNX, FP16 / INT8 quantisation). Real-time on-robot deployment is a core requirement. * Visual self-supervised representation learning experience on robot or 3D-vision tasks. * Legged-robot or whole-body control experience - locomotion, manipulation on a floating base, or the integration between them on quadrupeds or humanoids. * Physics-informed ML - hybrid models where learned components are constrained by known physics. * Experience building ML pipelines or infrastructure in a team setting. ## Description * Build the team's robot-learning stack from the ground up. This is a founding role; you are designing the training infrastructure, data pipelines, simulation environments, model architectures, and deployment workflows - not inheriting them. Multi-modal perception, scene understanding, and learned action generation work in tight coordination on the stack you help create. * Stand up ML infrastructure - training pipelines, experiment tracking, data versioning, reproducible sim-to-real workflows. * Train policies across manipulation, locomotion, and the whole-body control coupling between them. On legged platforms performing precision tasks, manipulation and locomotion are not separable - every arm motion shifts the centre of mass; the whole-body controller compensates in real time to maintain accuracy at the tool. Behavioural cloning, diffusion- and flow-matching action generation, reinforcement-learning fine-tuning. Cobots, industrial arms, and mobile platforms. * Deploy in stages - through a phased rollout strategy that builds production trust over time. Every real-world execution accumulates training data for continuous improvement. * Collaborate daily with mechanical engineers, perception engineers, robotics engineers, and manufacturing domain experts. Within-department rotation across home teams is expected., * Daily free lunch to keep you fueled and connected with the team ## 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) - [Fireside Chat: Deep Learning, Deep Impact: Harnessing AI for Language Innovation](https://www.wearedevelopers.com/videos/612-fireside-chat-deep-learning-deep-impact-harnessing-ai-for-language-innovation) - [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) - [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) - [From Perception to Autonomy: Building Agentic Edge AI Robots with ROS 2](https://www.wearedevelopers.com/videos/100295-from-perception-to-autonomy-building-agentic-edge-ai-robots-with-ros-2) - [Physical AI for the Next Wave of Industrial Digitalisation](https://www.wearedevelopers.com/videos/100039-physical-ai-for-the-next-wave-of-industrial-digitalisation) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [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) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)