Senior Robotics control engineer
Job description
About the role
This role defines the real-time behavior of dynamic robotic systems through control and estimation algorithms. The work targets developers, researchers, and enterprises building physical AI applications.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
Requirements
5+ years of professional experience developing control systems for dynamic robots deployed on real hardware.
Master's or PhD in Robotics, Controls, Mechanical Engineering, or a related field.
Deep expertise in control theory, including nonlinear control, model predictive control (MPC), LQR, optimal control, and whole-body control.
Strong background in state estimation with Kalman filters (EKF/UKF), particle filters, factor graphs, and Bayesian estimation.
Production-quality C++ for real-time control and Python for analysis, simulation, and tooling.
Solid command of robot kinematics, rigid-body dynamics, and spatial mathematics.
Hands-on experience with sensor integration and characterization, including IMUs, encoders, and force/torque sensors.
Proven track record of implementing and validating control algorithms on physical robotic systems, not only in simulation.
Nice to have
Experience with bipedal, quadruped, or humanoid robots that are highly dynamic, underactuated, and contact-rich.
Background in reinforcement learning or learning-augmented control for legged locomotion or manipulation.
Experience with whole-body control and contact dynamics, including contact estimation, impact modeling, and friction-cone constraints.
Familiarity with trajectory optimization frameworks and solvers such as OSQP, IPOPT, Crocoddyl, or custom implementations.
Proficiency with simulation environments such as MuJoCo, Drake, Isaac Sim, or equivalent.
Experience with real-time computing constraints, including deterministic execution, latency budgets, and embedded deployment.
A record of publications at top-tier venues such as ICRA, IROS, CoRL, RSS, and IJRR.
Skills & tools
C++ and Python for real-time control, analysis, and simulation.
Simulation platforms and robot operating environments.
Practical notes
Work is conducted in an office setting in the Fremont Office. Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
Control algorithms run in real time on embedded systems and must meet strict latency and reliability constraints. This role uses simulation, optimization, and sensor fusion to close the gap between prototype and production robot systems.
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.