Founding Robotics Engineer

About the Role

You will develop robotic and autonomous systems that connect sensing, computation, planning, controls, and physical hardware. Your work will span system architecture, algorithm development, embedded integration, simulation, and testing on real platforms.

The strongest candidates bring depth in at least one core robotics discipline and the systems knowledge to work across the full stack. You should be comfortable deriving an algorithm, implementing it in production software, diagnosing unexpected hardware behavior, and validating performance through repeatable experiments.

What You’ll Do

• Own robotics development from concept through deployment. Translate operational needs into system requirements, architectures, interfaces, and measurable acceptance criteria.
• Develop autonomy capabilities. Build and integrate algorithms for state estimation, localization, mapping, perception, motion planning, or control according to platform needs.
• Connect algorithms to physical hardware. Integrate sensors, actuators, embedded computers, and communication interfaces into reliable robotic systems.
• Build production robotics software. Develop modular, testable C++ and Python software with attention to timing, concurrency, resource constraints, and fault handling.
• Develop simulation and validation tools. Create software-in-the-loop and hardware-in-the-loop workflows to evaluate behavior before and during physical testing.
• Engineer for real operating conditions. Address sensor noise, calibration errors, communication delays, actuator limits, uncertain environments, and limited compute resources.
• Design and execute experiments. Instrument systems, collect data, establish performance baselines, and use results to guide improvements.
• Diagnose failures across disciplines. Trace problems through software, electronics, mechanics, and algorithms to identify root causes.
• Build dependable system behavior. Implement health monitoring, operating limits, fault detection, and appropriate recovery or safe-state behavior.
• Collaborate across engineering teams. Work with mechanical, electrical, embedded, and AI engineers to resolve system tradeoffs and deliver integrated hardware.

Required Qualifications

• Degree in Robotics, Computer Science, Mechanical Engineering, Electrical Engineering, Aerospace Engineering, or a related discipline, or equivalent demonstrated experience.
• Demonstrated ownership of a robotic or autonomous system tested on physical hardware.
• Strong programming skills in C++ and Python, including debugging and profiling on Linux.
• Strong foundations in linear algebra, coordinate transformations, probability, numerical methods, and rigid-body motion.
• Deep expertise in at least one of the following areas, with working knowledge of adjacent disciplines:
• Estimation and localization: sensor fusion, Kalman filtering, visual or inertial odometry, SLAM, or optimization-based estimation.
• Planning and controls: trajectory generation, motion planning, feedback control, dynamics, or model predictive control.
• Perception: geometric vision, 3D reconstruction, object detection, tracking, or scene understanding.
• Robotics systems: embedded integration, middleware, real-time execution, sensor interfaces, or distributed robotic software.
• Experience with ROS 2 or comparable robotics middleware, including communication, transforms, logging, and diagnostics.
• Practical experience integrating and calibrating sensors such as cameras, IMUs, LiDAR, GNSS, encoders, or force sensors.
• Ability to validate algorithms against physical measurements and explain discrepancies between simulation and real-world behavior.
• Clear technical communication and the ability to make progress through ambiguous engineering problems.

Preferred Experience

• Autonomous aircraft, mobile robots, manipulators, unmanned vehicles, or other complex electromechanical platforms.
• End-to-end ownership of a subsystem from mathematical formulation through implementation and field validation.
• Real-time systems, embedded Linux, microcontrollers, motor controllers, or deterministic communication.
• GPU programming, model optimization, or deploying perception models on constrained hardware.
• Learning-based robotics, imitation learning, reinforcement learning, or vision-language-action models, supported by physical deployment results.
• Integrating AI models or LLM-based task planning with validated low-level controls and explicit execution constraints.
• Simulation tools such as Isaac Sim, MuJoCo, Gazebo, or equivalent environments.
• System identification, actuator characterization, calibration optimization, or uncertainty analysis.
• Field testing, reliability improvements, and transitioning research prototypes into repeatable deployments.
• Peer-reviewed research, open-source contributions, robotics competitions, or independently built systems that demonstrate exceptional technical execution.



What Strong Candidates Can Demonstrate

We value technical depth, sound judgment, and evidence of working systems. Candidates should be prepared to discuss:

• A robot or autonomous subsystem they personally designed, implemented, and tested.
• The mathematical foundations and engineering tradeoffs behind their approach.
• Their specific contribution across algorithms, software, and hardware integration.
• A difficult failure, how they isolated its cause, and what they changed.
• Measured results such as localization error, tracking accuracy, task completion rate, control performance, latency, or operating reliability.
• How they established the conditions under which the system could operate successfully.