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The Science organisation at Wayve advances foundational research in embodied AI — building learning systems that can understand, act, and adapt in the real world through interaction. Within Science, the Behaviour Optimisation team focuses on one of the most intricate and impactful challenges in this space: learning robust, generalisable, and personalised behaviours for real-world agents. Our mission is to develop learning algorithms that enable our AI driver to act with intelligence and intent — grounded in perception, driven by experience, and adaptable to new contexts. We work at the intersection of reinforcement learning, generative modelling, behaviour cloning, and latent action inference, with a focus on sample efficiency, uncertainty awareness, and interpretability.
Job Responsibility:
Architect the future – Design and evolve models for efficient, robust, and adaptable autonomy, setting a high technical bar for quality and innovation
Accelerate research impact – Partner with team members to test, scale, and productionise research ideas - from architecture design to data strategy. Provide technical guidance and feedback on research design, implementation, and evaluation. Implement scalable, high-throughput training pipelines for models with temporal context and develop and evaluate novel data sampling strategies to accelerate training and generalisation
Get hands-on when it matters – Lead from the front by contributing directly to key system components, codebases, and experiments, especially during high-leverage moments. Contribute directly as an IC on core research and development tasks (~60-70% of time)
Disrupt thoughtfully – Challenge assumptions, ask sharp questions, and champion bold ideas that push us beyond incremental gains and toward breakthrough advances
Make things happen – Lead a high-performing, cross-functional team of applied scientists and ML engineers working across ML, RL, representation learning, planning, among many more. Work closely with the team manager to drive quarterly planning and execution of research-engineering initiatives, enabling rapid iteration and delivery in high-ambiguity environments. Translate ambiguity into action and ensure technical progress tracks with our mission
Champion change – Lead through ambiguity. Balance structure and adaptability to help your team navigate evolving priorities, novel research, and complex organisational change
Requirements:
Years of experience in applied ML/AI roles with strong hands-on contributions
Demonstrated track record of impactful technical work in one or more of: multimodal learning, reinforcement learning, generative models, latent action modelling, optimisation, or planning
Experience building large-scale ML infrastructure and working with high-dimensional temporal data (e.g., video, multi-sensor inputs)
Deep understanding of the end-to-end lifecycle of ML research and deployment
Strong Python and PyTorch engineering fundamentals, with experience developing research-grade, production-oriented tools
Proven ability to shape technical strategy and lead architectural design for ML systems
Publications at top-tier ML conferences such as NeurIPS, ICML, CoRL or ICLR
Clear and thoughtful communicator, capable of influencing technical direction and mentoring others without formal reporting lines
Nice to have:
Experience working in autonomous vehicles (AVs), robotics, simulation, or other embodied AI domains
Years of experience in a technical leadership or tech lead capacity
Contributions to open-source ML tooling or large-scale training infrastructure
Prior experience in startup-like or high-ambiguity environments, where adaptability and initiative are key
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