We’re seeking an exceptional AI / Robotics Research Engineer to design and build vision-based autonomous systems operating in GPS-denied, fast-changing, and high-velocity environments (e.g., UAVs, aerial robotics). You’ll work hands-on at the intersection of deep reinforcement learning, real-time computer vision, model predictive control (MPC), and robotics, creating novel ML models and systems – not just stitching together open-source libraries.
You will collaborate with our hardware, embedded systems, and mechatronics teams to deploy AI models in the loop – in real-world settings with real consequences.

REQUIREMENTS

• 3–7+ years of hands-on experience in AI/ML, robotics, or autonomous systems research (industry or PhD/postdoc).
• Deep knowledge of real-time computer vision, sensor fusion, and model-based control.
• Strong expertise in deep reinforcement learning, optimal control, and/or hybrid AI systems.
• Track record of designing custom ML models or training pipelines, not just reusing open-source architectures.
• Advanced Python and C++ proficiency; deep familiarity with PyTorch and/or TensorFlow.
• Hands-on experience with NVIDIA’s GPU toolkits for embedded or real-time AI deployment.
• Experience deploying in simulation and hardware-in-the-loop environments.
• Preferably experience in aerospace, defense, or other high-velocity robotics environments.

• Design and implement custom machine learning architectures for vision-based navigation and control.
• Develop and optimize real-time perception systems using CNNs, transformers, or event-based vision.
• Apply deep reinforcement learning and MPC to guide autonomous behaviors in dynamic environments.
• Architect robust sensor fusion pipelines with IMUs, event cameras, lidar, radar, and visual odometry.
• Build, train, and evaluate ML models in simulation and deploy them to embedded hardware.
• Integrate AI with traditional control theory to achieve closed-loop autonomy at scale.
• Use and optimize NVIDIA toolkits (CUDA, TensorRT, DeepStream, Jetson) for performance-critical workloads.