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

About Capstan Medical

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.

• 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 communicator who can explain ML clearly to controls engineers, clinical engineers, and physicians, document work effectively, and help the team decide when a model is good enough to trust.

• MS in Computer Science, Electrical Engineering, Robotics, or a related field. PhD is a plus.

• 4+ years of professional experience in computer vision or machine learning, classical or learned, including at least one system shipped to production on real-world data.

• Experience working with scarce, noisy, or expensive data, using synthetic data, augmentation, self-supervision, or physics priors to make up for it.

• Experience building ML on data from physical systems such as robots, cameras, medical imaging, or sensors.

• Ability to thrive in a fast-paced environment and own problems end-to-end.

• Ability to work onsite 3–4 days per week, split flexibly between our Santa Cruz, CA and West Menlo Park, CA offices, with at least 1 day per week in Santa Cruz.

• Ultrasound or echocardiography experience, strongly preferred. Fluoroscopy or X-ray experience is also valuable, including 2D/3D registration or digitally reconstructed radiographs.

• Experience tracking catheters, needles, surgical tools, or continuum robots in images.

• Sensor fusion and state estimation, such as Kalman filters or factor graphs.

• System identification or learned dynamics for control, including residual models on top of physics.

• Robotics software experience with ROS/ROS2 and on-device inference under latency constraints (ONNX, TensorRT).

• Data platform and MLOps experience, including practices like dataset versioning and experiment tracking.

• Medical device or safety-critical background, including validating ML under FDA design controls.

• Experience as an early ML engineer on a team, setting up the tools, data practices, and evaluation that others build on.

• Publications at venues such as MICCAI, IPCAI, CVPR, ICRA, or IROS.