About This Role
Our robots operate in real production environments where control reliability isn't optional — it's the difference between a deployed system and a demo. As a Senior Controls Engineer, you'll work directly on the low-level control and motion systems that make our bimanual wheeled robots safe, precise, and production-ready. This is not a paper-reading internship. You'll ship code that runs on real hardware, debug edge cases that only surface on the factory floor, and see the direct impact of your work on deployed robot performance.
What You'll Do
● Design and implement real-time supervisory control systems for smooth handoff between autonomous and teleoperated modes, including trajectory blending and forward-consistent motion during transitions.
● Improve inverse kinematics solvers for multi-arm manipulation — resolving singularities, boundary failures, and ensuring robust solutions across the full reachable workspace.
● Develop sensor fusion pipelines (IMU, odometry, vision) for precise mobile base localization and orientation control under tight angular tolerances.
● Integrate new actuators for bimanual arm systems — including motor driver communication, torque/position/velocity control mode configuration, gain tuning, and safety limit implementation
Required Qualifications
● Strong fundamentals in classical control theory (PID, impedance/admittance control, state estimation) with hands-on experience implementing controllers on physical hardware.
● Proficiency in Python and C/C++ for real-time embedded or robotic applications.
● Hands-on experience with inverse kinematics solvers and simulation environments (PyBullet, MuJoCo, Drake, or similar), including practical familiarity with IK libraries or custom Jacobian-based implementations.
● Experience with rigid-body kinematics, coordinate frame transformations (homogeneous transforms, SE(3), quaternion representations)
● Experience with sensor fusion techniques (Kalman filtering, complementary filters) using IMU, encoders, or similar proprioceptive sensors.
Preferred Qualifications
● Prior experience with ROS/ROS2, CAN bus communication, or motor control interfaces.
● Familiarity with trajectory smoothing and interpolation methods (spline-based planning, Ruckig, or similar time-optimal trajectory libraries).
● Experience with wheeled or mobile robot platforms, including human in the loop data collection framework
Benefits
● Medical, dental & vision plans ($0 payroll deduction)
● Daily meals stipend.
Interview Process
● Phone screen + 2 virtual technical interviews + onsite
● $100,000 - $200,000 annual salary + cash and stock awards + benefits
Expected Compensation
● The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.
Skills Required
- Strong fundamentals in classical control theory, including PID, impedance/admittance control, and state estimation, with hands-on experience implementing controllers on physical hardware.
- Proficiency in Python and C/C++ for real-time embedded or robotic applications.
- Hands-on experience with inverse kinematics solvers and simulation environments such as PyBullet, MuJoCo, Drake, or similar.
- Experience with rigid-body kinematics and coordinate frame transformations, including homogeneous transforms, SE(3), and quaternion representations.
- Experience with sensor fusion techniques, including Kalman filtering and complementary filters, using IMU, encoders, or similar proprioceptive sensors.
- Prior experience with ROS/ROS2, CAN bus communication, or motor control interfaces.
- Familiarity with trajectory smoothing and interpolation methods, such as spline-based planning or Ruckig.
- Experience with wheeled or mobile robot platforms, including human-in-the-loop data collection frameworks.
What We Do
Deft AI, Inc., operating as Deft Robotics, deploys wheeled humanoid robots and robot-foundation-model-based work cells in manufacturing facilities. Its systems integrate with factory workflows to automate difficult tasks that still require people, including final assembly, cable handling, line feeding, and kitting. The company focuses on automotive and electronics manufacturers, combining robotics, AI, and on-site integration to help factories improve throughput and address labor-intensive processes.
