Senior Machine Learning Engineer

Diffusion World Models & Robotics – ML Research EngineerONLY LOCALS or who can work ONSITE. No H1s NO C2CLocation: Oakland, CA / Bay Area, CAWork Arrangement: OnsiteEmployment Type: Full-Time (FTE) or ContractWhat We’re Looking ForWe’re looking for highly technical ML researchers/engineers passionate about improving machine learning algorithms at the intersection of diffusion world models and robotics.You’ll work on interactive, action-conditioned world models and use them as simulators, feature extractors, and training environments for real robot policies and model-based reinforcement learning.This is a highly experimental research environment where failed experiments are expected. The goal is to learn quickly from each experiment and continuously improve performance on real hardware.What You’ll DoDevelop and improve interactive world models, including diffusion backbones, action conditioning, data strategies, and downstream policies.Implement state-of-the-art diffusion and robot-learning algorithms.Design losses, reward functions, and reinforcement/preference-based fine-tuning methods.Run high-volume experiments and ablations across model architecture, features, data, and conditioning strategies.Build ML pipelines connecting world models to real robots, including data collection, fine-tuning, and policy deployment.Work with Robots-as-a-Service (Robots-aaS) and design partner systems.Optimize training and inference using the latest capabilities of modern GPUs.Own the ML stack end-to-end, from data pipelines and experimentation to deployment.Collaborate closely with research and product engineering teams.Must-Have SkillsPhD or equivalent research experience + 2+ years relevant research/engineering experience, OR 4+ years software engineering experience with 2+ years of relevant ML experience.Hands-on experience with one or more:Diffusion ModelsWorld ModelsModel-Based Reinforcement Learning / DreamerVision-Language-Action (VLA) PoliciesStrong PyTorch experience; TensorFlow or JAX also relevant.Experience owning ML/research projects end-to-end.Strong understanding of ML experimentation, training, and evaluation.Willingness to work across the full ML pipeline, from data to real-robot deployment.Highly experimental and research-oriented mindset.Nice to HaveSim-to-real experience.Real-world robotics or robot-learning experience.Experience deploying ML policies on physical robots.GPU optimization and ML performance engineering experience.Experience with reinforcement learning or preference-based fine-tuning.