Senior AI/ML Research Engineer – Model development

Job description

Job responsibilities

• Develop, fine-tune, and evaluate the AI/ML models—including foundation and multimodal models—that enable the system to perceive the surgical scene and translate intent and observations into safe, performant behavior.
• Establish strong baselines by reproducing relevant state-of-the-art approaches, then iteratively advance them with in-house models and components while keeping interfaces stable.
• Build and maintain training and data pipelines that combine real demonstration data with simulation, and establish human-in-the-loop pipelines for continuous model improvement.
• Define and run evaluation for model performance, repeatability, and safe failure/abort behavior, and establish the path from offline evaluation on recorded data to robust, real-time integration.
• Partner with data and annotation teams to shape label taxonomies, quality control, and the data pipeline that feeds the models.
• Collaborate across AI/ML research, robotics, software, and data engineering to align on interfaces and deliver models that enable rapid prototyping and learning while building toward a product solution.

Job requirements

• MS or PhD in CS, EE, Robotics, or a related field, with 5+ years of applied AI/ML research experience in areas such as robot learning, embodied AI, control, or sequential decision-making.
• Hands-on experience training policies or models from data, including imitation/behavior cloning and reinforcement learning, and fine-tuning pretrained models.
• Experience with vision-action (VA), vision-language-action (VLA), or goal/intent-conditioned models, including models that produce action or control outputs.
• Familiarity with world models and self-supervised predictive architectures (e.g., JEPA-style models, MAE, DINO) for learning dynamics and latent representations to support planning and control.
• Comfort building training and evaluation loops over both simulated and real-world data.
• Strong software and ML-engineering skills in Python and C++, with proficiency in one or more of PyTorch/TensorFlow/JAX.
• A research-and-prototyping mindset: comfortable working in ambiguity, framing open-ended problems, running rapid experiments, and reading and reproducing recent papers to pull promising techniques into practice.
• Sound judgment about the path from prototype to product: writing code others can build on, knowing when to optimize versus when to move fast, and thinking ahead about data quality, evaluation, and robustness even at…