Machine Learning Engineer, Wayve

• Experience applying reinforcement learning techniques, including offline RL, reward modeling, RLHF-style approaches, or similar
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX, Ray, or equivalent)
• Experience building ML pipelines or large-scale training workflows in production or research environments
• Strong understanding of simulation environments and/or real-world behavioural data
• Ability to design and run experiments, analyse results, and turn insights into actionable improvements
• Strong problem-solving skills and the ability to work effectively in cross-functional teams

Desirable

• We’re looking for a Machine Learning Engineer with strong experience in reinforcement learning (RL), reward modeling, and large-scale ML systems to advance how we train, evaluate, and deploy embodied AI behaviors
• This role sits at the intersection of ML engineering, applied RL research, and ML systems, working on the frameworks that guide how our autonomous agents learn from data, simulation, and real-world experience
• As an MLE on the Accelerated Learning Loop team, you will design and optimise end-to-end pipelines for training reward models and RL agents, ensuring they are reproducible and high-throughput
• Develop tooling for data processing, annotation, and inference within RL workflows
• Build, refine, and deploy reward models that encode safe, interpretable, and effective driving behaviours
• Integrate reward models with diverse data sources: real-world trajectories, simulation, and synthetic datasets
• Conduct ablations, hyperparameter explorations, and controlled studies to analyse how reward structures, data composition, and training dynamics affect policy performance
• Diagnose failure modes, investigate emergent behaviours, and iterate on reward objectives to improve reliability
• Work closely with RL scientists to translate research ideas into scalable engineering solutions
• Partner with evaluation teams to integrate reward and RL models into offline/online testing suites and simulation frameworks
• Establish best practices around code quality, reproducibility, and deployment readiness
• Build internal tools and visualisations that enable faster debugging, deeper insights, and more efficient iteration across the RL and reward modeling stack
• This role is ideal for someone who enjoys building systems and running fast, grounded experiments. Someone who is motivated by delivering real impact on the behaviour of embodied AI systems in the real world