Senior Machine Learning Engineer, Perception and Controls

Workplace Type: Hybrid — Onsite 3–4 days per week across Santa Cruz and West Menlo Park, CA, with at least 1 day per week in Santa Cruz

Reports To: Robotics & Controls Manager

At Capstan Medical, we're building one of the most exciting and innovative companies in medtech: a first-of-its-kind robotic platform for minimally invasive heart valve treatment.

Our team combines surgical robotics, catheter-based delivery, and next-generation implants to turn complex procedures into safer, lower-stress solutions for patients and clinicians. We're a collaborative, hands-on team that moves fast, wears multiple hats, and believes the best ideas can come from anyone.

Based in Santa Cruz, our workspace blends Bay Area innovation with a lifestyle near trails, beaches, and open space—a great place to do meaningful, career-defining work.

Teach a heart valve robot to see where it is.

We're hiring a Senior Machine Learning Engineer to join our robotics and controls team. You'll help our robot understand where its devices are inside the patient, using intraprocedural imaging such as fluoroscopy and echocardiography. That means building perception for image-to-robot registration and pose estimation, and taking it from research into a clinical system.

This is not a model-in-a-notebook role. Your work runs on a real-time robotic system, informs what physicians see during the procedure, and ships under FDA design controls.

You'll also help shape how we collect, organize, and learn from our robot and imaging data, and use it to improve our device models and controllers. You'll have a large say in how ML is done here, from the tools and standards we adopt to which problems we tackle first, and you'll be the person the rest of the team turns to for ML judgment.

• Develop methods to register intraprocedural imaging, such as fluoroscopy and echocardiography, to the robot.
• Estimate the pose of catheters and implants from medical images.
• Combine image-based estimates with robot kinematics and other sensing, and quantify the uncertainty of each estimate.
• Build data pipelines that capture, align, label, and version imaging and robot data for training and evaluation.
• Use simulation and synthetic data where real clinical data is limited.
• Fit and improve models of device behavior from data, and work with controls engineers to turn them into better control.
• Work with clinical engineers and physicians to learn what the images show, what matters in the procedure, and what accuracy is good enough.
• Read the literature, prototype what looks promising, and tell us plainly what works on our data and what doesn't.
• Deploy models on the robot and meet real-time latency budgets.
• Write the validation, test datasets, and design documentation that let learned components pass FDA design controls.

• Real-World Machine Learning: You've taken models out of notebooks and into products that run on real data, with all its noise, drift, and edge cases.
• Computer Vision: Strong background in classical and learned computer vision, especially pose estimation, segmentation, tracking, or 2D/3D registration.
• 3D Geometry: Solid grasp of rigid transforms, rotations, camera projection models, and calibration.
• Programming: Strong Python and PyTorch skills. Comfortable reading and writing C++.
• Data Engineering: You build pipelines, datasets, and evaluation harnesses that others can trust and reuse.
• Research and Prototyping: You keep up with recent papers, can tell which ideas will survive contact with our data, and enjoy turning them into working prototypes.
• Problem-Solving: Comfortable with high ambiguity. You can take a fuzzy goal, break it into experiments, and pick a direction without a spec.
• Communication: Excellent…