Staff / Senior Machine Learning Engineer, Reinforcement Learning

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. About Our Engineering TeamsThe Driving Core team develops the learning methods that turn diverse driving data into robust closed-loop behavior. You'll be a technical owner for reinforcement learning within the group, working closely with researchers and engineers across AV Core, Simulation, Evaluation, and Product Engineering. Success means producing measurable improvements in driving behavior. The Core Model Safety team develops the core model competencies that enable safe, driverless operation. In this team, you'll lead the technical direction and delivery of a learned emergency trajectory model for low-frequency, high-consequence manoeuvres such as evasive steering and emergency braking, taking the programme from problem definition through modelling, evaluation, integration, and evidence for deployment. Your day-to-dayAs 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. What You’ll Be Working OnShape 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. You should apply ifEssentialYou 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. DesirableExperience 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. Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More About WayveWayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work - Locations & Flexible WorkingOur main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. The Interview ProcessOur process is clear and respectful of your time: Initial call / recruiter screen [ 30 mins] Competency Interviews [hiring manager interview and applied ML interview; 2 hours total] Deep-dive technical interviews [programming, systems design & PyTorch debugging / technical leadership interviews; 3 hours total] Final interview: mission & values alignment [Director or VP interview; 45 minutes]. We’ll always explain the format and work around your availability.