- Design, implement, and verify robotic control algorithms for Capstan's
catheter-based surgical platform — kinematics, dynamics, teleoperation, and motion control.
- Develop forward and inverse kinematic models for continuum and tendon-
driven mechanisms, and validate them against real-world data.
- Build trajectory engines, drive modes, and constraint-aware motion
algorithms that translate surgeon intent into safe, precise catheter motion.
- Design and execute characterization studies on actuators, tendon
transmissions, and full system performance; translate experimental results into models, parameters, and acceptance criteria.
- Define algorithm-level performance specifications and hazard-driven fault
detection thresholds; partner with Systems and Quality to support design verification and regulatory submissions
- Translate clinical and user needs into design requirements, prototype
rapidly on real hardware, and carry features through verification to product launch
- Drive technical direction for path planning, catheter control, or robotics
algorithms across multiple hardware programs (increased level of architectural ownership and cross-program scope for Staff)
- Mentor engineers on the team, lead design reviews, and contribute to
hiring and onboarding (increased level of mentorship for Staff)
- Document your work through design reviews and released DHF
documentation, and identify gaps and pitch in as needed to keep the team moving
Strongly Preferred
- Strong foundation in robot kinematics and dynamics - forward/inverse kinematics, Jacobians, rigid-body dynamics, and ideally continuum, concentric tube, or tendon-driven systems
- Fluent with classical and modern control -- PID, feedforward, model-based, optimal/MPC, impedance/admittance -- and the judgment to know when each is appropriate
- Hands-on experience implementing control algorithms in modern C++ (14/17/20) for real-time, safety-critical systems, with Python for rapid prototyping, data analysis, and tooling
- Motion planning and trajectory generation experience — constrained nonlinear optimization, collision-aware planning, or task-space control paradigms
- State estimation and sensor fusion experience — Kalman/EKF/UKF, particle filters, or nonlinear observers, with a working understanding of noise models and sensor calibration
- Comfortable characterizing real electromechanical systems on the bench; you can design the experiment, run it, analyze the data, and turn it into a model.
- Close-to-hardware sensibility: encoders, motor controllers, force sensors, brakes, IMUs -- you understand the gap between textbook systems and real ones
- Strong object-oriented design instincts, with the discipline to factor algorithm code so it can be unit-tested, reviewed, and maintained as the team scales
- Medical robotics or safety-critical systems background -- you know why "it works in sim" or "it works most of the time" isn't good enough
- Understanding of how early design decisions cascade into downstream cost; ability to make smart calls on clinical workflow, regulation, and risk management to save time and effort later