Senior Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer to lead the development of a neural welding simulator - a learned world model that captures the visual and physical dynamics of welding and enables large-scale RL training. This role sits at the intersection of generative modeling, robotics, and applied physics. It is research-heavy by design, while still grounded in production reality.

We are hiring two Senior Machine Learning Engineers with complementary specializations:

- 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.

- Develop reinforcement learning approaches for optimizing welding decisions and process outcomes.

- Define state, observation, action, and reward representations based on measurable manufacturing objectives.

- Train and evaluate policies using learned world models, traditional simulation, offline datasets, and controlled real-world experiments.

- Develop offline, model-based, or constrained RL methods suitable for limited and expensive physical interaction.

- Optimize across competing objectives such as weld quality, cycle time, reliability, energy use, and equipment constraints.

- Design methods that account for uncertainty, distribution shift, delayed outcomes, and sparse or imperfect reward signals.

- Diagnose reward exploitation, unsafe behavior, policy instability, and model exploitation.

- Establish reliable offline and real-world policy evaluation methods.

- Partner with controls, welding, robotics, world-model, data, and ML infrastructure engineers.

- Translate research prototypes into dependable training, evaluation, and deployment systems.

- 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.