Build the intelligence behind the next driving decision.
We’re building a reinforcement-learning driving planner for complex, interactive road scenarios. We’re looking for a researcher to help take it from simulation to real vehicles.
You’ll develop policy models, rewards, and training methods. You’ll define how driving behavior is evaluated, analyze failures in closed loop, and validate improvements on the road. You’ll work in a small team at Mobileye and collaborate with control and other algorithm teams.
This is a high-impact role with direct influence on a core part of Mobileye’s
driving technology and its future products.
Desenvolve algoritmos quânticos e fluxos híbridos CPU–GPU–QPU para problemas de otimização e simulação, avaliando desempenho em simuladores e hardware e orientando equipes em computação quântica, HPC e IA.
Research and develop reinforcement-learning driving planners, from policy design and simulation training to closed-loop evaluation, failure analysis, and validation of improvements on real vehicles.
Research and deploy trading algorithms by analyzing market data, developing statistical signals and tools, and improving simulations to support high- to mid-frequency trading and live execution.
Post-train open-source LLMs for specialized customer tasks by turning domain-specific data into reward functions, agent environments, evaluations, and training pipelines that improve production models.
As a Research Scientist, you'll advance frontier AI by creating high-quality data and environments, optimizing pipelines, and collaborating with experts to enhance model performance.
