# Staff / Senior Machine Learning Engineer, Reinforcement Learning **Company**: Wayve **Location**: Sunnyvale, California **Work arrangement**: hybrid **Experience**: staff **Job type**: full-time **Category**: Engineering **Industry**: Automotive **Apply**: https://jobs.ashbyhq.com/wayve/5df6d6f2-5bf8-4623-97e3-4e82a940c674?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_195f921b-c0c ## Description We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Every role here, whatever the team, plugs into that. As a Senior / Staff Machine Learning Engineer in Wayve's AV Core organisation, you will advance reinforcement learning methods for end-to-end driving models. You'll identify where learning from reward or feedback can improve beyond behavior cloning, then take promising ideas from design through large-scale experiments, rigorous evaluation, and integration into our best driving models. ## Responsibilities - Shape and execute the reinforcement learning roadmap for Driving Core / Core Model Safety, selecting problems and methods against clear behavioral gaps and measurable success criteria. - Develop and evaluate post-behavior-cloning optimization methods, including offline and off-policy reinforcement learning as well as other reward-guided approaches; design the regularization, data strategy, and diagnostics needed to make policies reliably better. - Help improve the reward models and related learning signals used to train and evaluate driving policies, working with partner teams to strengthen their quality, scalability, and downstream usefulness. - Build robust training and experimentation workflows using large-scale driving data; diagnose distribution shift, objective misspecification, optimization instability, and data or evaluation bias. - Define evidence across offline metrics, open-loop tests, closed-loop simulation, and on-road evaluation, and distinguish genuine policy improvement from benchmark overfitting. - Productionize successful methods in the shared ML stack, communicate decisions and results clearly, and raise the technical bar through design reviews, code reviews, and mentoring. ## Requirements - Essential: - You have a strong track record developing and experimentally validating reinforcement learning or closely related sequential decision-making methods on complex, high-dimensional problems. - You have a deep understanding of modern reinforcement learning fundamentals, including policy and value learning, off-policy learning, function approximation, distribution shift, and the failure modes of learned objectives. - You have hands-on experience with behaviour cloning, reinforcement learning, or related methods. - You're proficient in Python and PyTorch, with strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems. - You have excellent experimental judgement: able to turn an ambiguous behavioral problem into falsifiable hypotheses, useful metrics, disciplined ablations, and clear technical decisions. - You bring senior-level ownership and collaboration: able to lead a substantial technical area, work across research and engineering boundaries, and bring others along through clear written and verbal communication. - Desirable: - Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training of large neural policies. - Experience in autonomous vehicles, robotics, control, or another domain where policies interact with safety-critical physical systems, including an understanding of motion planning, vehicle dynamics, control, or collision avoidance. - Experience with closed-loop simulation, off-policy evaluation, uncertainty or calibration, and evaluation under rare or shifted conditions. - Experience training multimodal, transformer-based, or generative policy models at scale. - Proficiency in C++, CUDA, distributed training, or performance optimization for production machine learning systems. ## Benefits - Salaries benchmarked against the market annually - Meaningful equity, sharing in the ownership and long term success of Wayve - Relocation support and visa sponsorship where applicable - Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs - Learning and development budgets with support for training, conferences and growth - Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more ## Skills ### Required - reinforcement learning - Python - PyTorch - machine learning - software engineering ### Nice to have - offline reinforcement learning - imitation learning - reward modeling - preference learning - post-training of large neural policies - autonomous vehicles - robotics - control - motion planning - vehicle dynamics - collision avoidance - closed-loop simulation - off-policy evaluation - uncertainty - calibration - evaluation under rare or shifted conditions - multimodal - transformer-based - generative policy models - C++ - CUDA - distributed training - performance optimization --- Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/wayve/5df6d6f2-5bf8-4623-97e3-4e82a940c674?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)