Senior Machine Learning Engineer (Reinforcement Learning/World Model)
Path Robotics· Engineering
Hybrid (Columbus, OH / Remote), Remotefull-timePosted 2mo ago
About the role
Senior ML Engineer roles focused on building a neural welding simulator and learned world models for RL in complex welding/manufacturing environments. Two complementary specializations: World Models and Reinforcement Learning.
Responsibilities
- Build action-conditioned world models that predict how the welding process evolves under changes to robot motion and process parameters.
- Model relationships among inputs, system state, physical dynamics, and resulting weld quality.
- Develop multimodal models using data such as video, 3D scans, thermal measurements, electrical signals, robot state, and process parameters.
- Explore latent dynamics, video prediction, generative modeling, and spatiotemporal representations.
- Improve long-horizon rollout accuracy, physical plausibility, temporal consistency, and computational efficiency.
- Quantify model uncertainty and identify conditions under which predictions are unreliable.
- Validate learned predictions against real-world welding data.
- Integrate the model into RL, planning, process-optimization, evaluation, and synthetic-data workflows.
- Prevent downstream optimization systems from exploiting inaccuracies in the learned model.
- Translate promising research into scalable training and inference systems.
- For RL specialization: Develop reinforcement learning approaches for optimizing welding decisions and process outcomes; define state/observation/action/reward representations; train/evaluate policies with various data sources; develop offline/model-based/constrained RL methods; optimize across objectives; handle uncertainty and distribution shifts; diagnose reward exploitation and policy stability; establish offline and real-world policy evaluation methods; collaborate with controls, welding, robotics, world-model, data, and ML infrastructure teams; translate research prototypes into dependable systems.
Qualifications
- Master’s or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.
- Experience developing and deploying reinforcement learning algorithms on real-world systems.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Experience with simulation environments (e.g., MuJoCo, Isaac Gym).
- Solid understanding of probability, statistics, and optimization.
- Experience with training and deploying ML models in production systems.
Skills
Required:World models / generative modelingReinforcement learning
Nice to have:Simulation and evaluation
Benefits
- Daily free lunch
- Flexible PTO
- Comprehensive medical, dental, and vision coverage
- 6 weeks fully paid parental leave, plus 6–8 weeks for birthing parents (12–14 weeks total)
- 401(k) retirement plan through Empower
- Generous employee referral bonuses
Last updated Aug 7, 2026. Interested? Apply on the company site.
About Path Robotics
Path Robotics | Physical AI for Manufacturing
Path Robotics – Physical AI for manufacturing. Obsidian, our foundational AI, powers Path's Intelligent Welding Cells and Rove. 4x productivity, 30%+ lower cost, $0 capex.
Welding CellsRoveObsidianPhysical AIManufacturingAutomation
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