Senior Robotics Control Engineer

Foundry Robotics•San Francisco, CA

7d•Onsite

About The Position

Foundry Robotics is building an AI-native robotics manufacturing company focused on deploying advanced assembly and production capability for leading robotics companies and national-security-critical hardware. Basically, we’re building robots that build robots. Factory OS is building software that runs factories. Our platform spans cloud backends, on-prem services, and edge devices deployed on the factory floor. The operators, supervisors, and managers who use our tools need interfaces that are fast, reliable, and intuitive—even when network conditions aren’t perfect and the environment is demanding. The role The Senior Robotics Control Engineer will design and develop real-time control systems for advanced robotic manipulators, with a focus on torque control, motion planning, and state estimation. This role involves building optimization-based control algorithms, integrating sensor fusion across multiple sensing modalities, and driving controller performance from simulation through hardware validation. Working closely with the Autonomy, Hardware, and Testing teams, the engineer will develop scalable, high-performance robotics solutions that enable precise, reliable, and intelligent manipulation in real-world environments.

Requirements

5+ years of experience implementing optimization-based multi-DOF control algorithms (inverse dynamics, trajectory optimization) on real robotic systems
Experience building state estimation solutions for real-time robotic systems such as (E)KFs, Particle Filters, Factor Graphs, etc.
Experience with various sensors (encoders, torque sensors, force-torque sensors, IMUs) and sensor fusion
Deep understanding of spatial kinematics, dynamics, and related mathematical concepts
A proven track record iterating on complex multi-DOF systems, with emphasis on torque control and low impedance
Fluency in modern C++ and Python

Nice To Haves

MPC / receding-horizon control on real systems
Contact-rich manipulation, grasping, or force-controlled assembly
Rigid-body dynamics libraries (Pinocchio, Drake, MuJoCo) and optimization solvers
ROS 2 and real-time Linux
Experience with Machine Learning as it relates to control or state estimation (learned dynamics models, residual policies, imitation learning)

Responsibilities

Design and develop real-time C++ code control of robotic arms, with emphasis on torque control and low impedance.
Build model-based planning and control components: trajectory optimization, inverse dynamics, and whole-arm motion control.
Develop state estimation for real-time manipulation, fusing encoders, torque sensors, force-torque sensing, and IMUs.
Own controller performance end-to-end: simulation validation, hardware bring-up, tuning, and regression testing.
Design scalable solutions that integrate with Autonomy, Hardware, and Testing systems at Foundry.