Robotics Control Engineer

Essential functions

• Develop teleoperation capabilities for controlling robots across manipulation, mobility, and loco-manipulation tasks.

• Design intuitive operator experiences, including input mappings, visualizations, feedback loops, recovery flows, and workflows that reduce cognitive load and improve operator confidence.

• Integrate input devices such as VR controllers, gloves, haptic devices, motion capture systems, and custom operator stations into the robot software stack.

• Translate noisy, high-frequency human input into smooth, stable, physically feasible robot commands with predictable behavior.

• Build robust teleoperation infrastructure for real-time data streaming, calibration, replay, debugging, logging, operator training, and high-quality demonstration capture.

• Develop shared-autonomy and assistive-control features that blend human intent with robot capability to improve precision, safety, and ease of use.

• Create safe operating boundaries, limiters, alerts, emergency/recovery flows, and clear state feedback so robot operators can use teleoperation safely.

• Define and improve quality metrics for teleoperation systems, including latency, reliability, observability, operator success rate, demonstration quality, and failure modes.

• Stress-test teleoperation systems on real hardware and in simulation, then drive reliability improvements based on operator feedback and observed failures.

• Work closely with controls, manipulation, hardware, and operations teams to ensure teleoperation requirements shape robot design and field workflows.

Qualifications

• Strong robotics fundamentals, including kinematics, coordinate frames, control interfaces, state estimation basics, and real-time system behavior.

• Experience building a subset of the following: teleoperation systems, operator UIs, shared-autonomy tools, human-robot interfaces, or robot learning data-collection pipelines.

• Ability to write reliable software in C++ and/or Python, with experience in ROS/ROS2, real-time data streaming, or robotic middleware.

• Good intuition for human-in-the-loop systems: how operators perceive robot state, issue commands, recover from errors, and build trust.

• A quality mindset: clean abstractions, testing, logging, observability, performance tuning, and tools that remain usable under pressure.

• Strong debugging skills across the stack, from operator input devices to robot motion, sensors, networking, and hardware behavior.

• Comfort iterating quickly on real robots, learning from operator feedback, and turning prototypes into robust capabilities.

• Clear communication and collaboration with cross-functional teams.

Would be a plus

• Hands-on experience building or debugging software for real robotic hardware, ideally in manipulation, mobile manipulation, humanoids, or dexterous systems.

• Experience designing safe workflows for hardware-facing systems where latency, instability, poor feedback, or misuse can create operational risk.

• Experience collecting high-quality demonstrations for imitation learning, behavior cloning, or other robot learning workflows.

• Exposure to intent inference, haptics, force feedback, motion capture, VR/AR interfaces, or custom control devices.

• A product-minded approach to robotics: caring not only that the robot moves, but that control is intuitive, safe, reliable, and pleasant for operators to use.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.