Research Engineer, Robotics - Roboticist

Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.

Our base salary range is $128,880 - $193,320, and in addition we have generous bonus plans to provide a competitive compensation package.

Who You Are:

You will own the control stack on our physical robots and the path from simulation to them. This is first and foremost a controls role: you will bring up and tune real-time, torque-level control on arms, multi-fingered hands, bimanual systems and humanoids, make it stable and safe enough that researchers run experiments on hardware every day, and build the interfaces that learned policies plug into. Experience with humanoid whole-body control and with Unitree platforms (G1, H1 and similar) is a strong plus, as humanoid and whole-body work is a growing share of the lab. You will also design and build the hardware the research needs, from end-effectors to test rigs. You will work closely with research scientists and the simulation team so that what is learned in simulation shows up as reliable behaviour on hardware.

Who We Are:

The Robotics team at Ai2, led by Dieter Fox, uses scalable simulation to study how foundation models scale and generalize for robotics. We focus on manipulation, humanoid and whole-body control, large-scale simulated training, sim-to-real transfer, robot foundation models and skill learning. We publish at CoRL, ICRA, RSS, CVPR, NeurIPS, ICLR and ICML, and everything we build is open source.

Your Next Challenge:

• Build and own the real-time control stack: joint, impedance, operational-space and torque control on torque-controlled arms, hands and humanoids, with motion planning, safety layers and the interfaces that learned policies plug into, across embodiments.

• Bring up, tune and stabilize whole-body controllers for humanoids and bimanual systems: QP-based or MPC-based whole-body control, state estimation (IMU, encoders, force/torque), contact handling and fall or fault recovery, and the sim-to-real transfer of learned locomotion and loco-manipulation policies.

• Lead the setup, calibration, maintenance and tuning of our robots: arms, grippers, multi-fingered hands, humanoids, cameras and tactile sensors, across single-arm, bimanual, mobile and legged platforms.

• Diagnose stability, latency and contact problems across hardware, firmware, control and learned policy, using system identification and dynamics models to turn algorithms into reliable real-world behaviour.

• Design and build the hardware the lab needs: end-effectors, fingertips, sensor mounts, fixtures and test rigs, from CAD through 3D printing or machining, including integrating cameras, tactile sensors and hands.

• Build the runtime and tooling around the robots: teleoperation and data collection, logging, monitoring, deployment and operator-facing tools, so the platform scales across robots, tasks and people.

• Contribute to open-source releases and publications.

What You’ll Need:

Minimum Qualifications:

• Master’s with 3+ years of experience, or PhD, in robotics, control or a related field. Strong candidates with a bachelor’s and 3+ years of industry experience are also considered.

• Controls: you have implemented and tuned real-time controllers (joint, impedance, operational-space or torque control) on physical robots, with a solid grounding in rigid-body dynamics, control theory, stability analysis and system identification. You can explain why a controller went unstable and fix it.

• Whole-body and floating-base control: you understand floating-base dynamics, contact constraints and inverse dynamics, and have worked with at least one of QP-based whole-body control, operational-space control or MPC, on hardware or in a high-fidelity simulator.

• Manipulation: substantial hands-on experience with real-robot manipulation, including bring-up, calibration and control of arms with grippers or hands.

• System design: you have designed robot software systems end to end (control loops at 500 Hz to 1 kHz, state estimation, data flow, and the interfaces between planning, perception and learning) and can reason about latency, reliability and failure modes. Strong Python; solid real-time C++.

• Mechanical design: you have designed and built hardware that was used on a real robot, such as end-effectors, mounts, fixtures or rigs. CAD required (SolidWorks, Fusion 360, Onshape or similar), with design-for-manufacturing experience for 3D printing or machining.

• Robot learning: you have trained or deployed learned policies (imitation learning, RL or VLA) on real robots, and are comfortable with PyTorch and at least one simulation ecosystem (Isaac Lab, MuJoCo or similar).

• Debugging across the full stack, from perception through control to hardware, and clear communication with a structured, systems-oriented approach.

Preferred Qualifications:

• Strong plus: humanoid whole-body control on hardware, including locomotion, loco-manipulation, balance and contact-rich tasks, and hands-on experience bringing up and deploying on Unitree platforms (G1, H1 or similar) or comparable humanoids.

• Depth in control theory: Lyapunov and passivity-based methods, contact-stable and compliant control, dynamical-systems approaches to motion generation, and reduced-order models (centroidal dynamics, SRBD, LIPM), with experience applying them on hardware.

• Sim-to-real for whole-body policies: training RL locomotion or loco-manipulation policies in GPU-accelerated simulators (Isaac Lab, MuJoCo Warp) and getting them to run stably on a real humanoid, including domain randomization, actuator modelling and safety wrappers.

• Whole-body state estimation and sensor fusion (IMU, joint encoders, force/torque, contact estimation, Kalman filtering).

• Experience with bimanual systems, multi-fingered hands or tactile sensing.

• Teleoperation systems (VR, leader-follower arms, hand and body tracking, whole-body retargeting) and large-scale data collection.

• Robotics middleware and infrastructure: ROS 2, DDS or Zenoh, real-time Linux, containerization, CI/CD.

• Hand-eye and multi-camera calibration, depth sensing and 3D perception on hardware.

• Open-source contributions or publications at robotics venues (CoRL, RSS, ICRA, IROS, Humanoids).

Physical Demands and Work Environment:

The physical demands described here are representative of those that must be met by a team member to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the functions.

• Must be able to remain in a stationary position for long periods of time.

• The ability to communicate information and ideas so others will understand. Must be able to exchange accurate information in these situations.

• The ability to observe details at close range.

• Can work under deadlines.

A Little More About Ai2:

Ai2 is a Seattle based non-profit AI research institute founded in 2014 by the late Paul Allen. Our mission is building breakthrough AI to solve the world’s biggest problems. We develop foundational AI research and innovation to deliver real-world impact through large-scale open models, data, robotics, conservation, and beyond.

In addition to Ai2’s core mission, we also aim to contribute to humanity through our treatment of each member of the Ai2 Team. Some highlights are:

• We are a learning organization – because everything Ai2 does is ground-breaking, we are learning every day. With a wide variety of world-class AI experts as guest speakers, and our commitment to your personal on-going education, Ai2 is a place where you will have opportunities to continue learning alongside your coworkers.

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