Senior Machine Learning Engineer, Perception and Controls
Senior Machine Learning Engineer at Capstan Medical builds perception for a robotic heart-valve treatment platform: image-to-robot registration and pose estimation from fluoroscopy/echocardiography, fused with robot kinematics, deployed in real time under FDA design controls. Hybrid role, onsite 3-4 days/week across Santa Cruz and West Menlo Park, CA.
- Compensation
- Not disclosed
- Location
- West Menlo Park, California
- Work setup
- Onsite
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 4+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- MS in Computer Science, Electrical Engineering, Robotics, or a related field
- 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
- 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 Santa Cruz, CA and West Menlo Park, CA offices, with at least 1 day per week in Santa Cruz
Nice to have
- PhD
- Ultrasound or echocardiography experience (strongly preferred); fluoroscopy or X-ray experience 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 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 tools, data practices, and evaluation
- Publications at venues such as MICCAI, IPCAI, CVPR, ICRA, or IROS
Full description
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.
About the Role
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.
What You'll Do
-
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.