Perception Engineer, Autonomy (Air Domain)

Preferred

• Experience with online, real-time learning: models that adapt during operation, not just offline training.
• Background in adaptive control or learning-based estimation (e.g. concurrent learning, Lyapunov-based learning, Bayesian or recursive least-squares parameter estimation).
• Experience learning from intermittent, delayed or sparse feedback, where ground truth arrives late or only sometimes.
• Behavior and intent prediction for other agents, including models of non-cooperative or adversarial actors.
• Deploying small neural networks in C++ without heavy ML frameworks, under tight latency and compute budgets.