Research Manager, On Road Innovation

The role 

Science is the team that is advancing our end-to-end autonomous driving research. The team’s mission is to accelerate our journey to AV2.0 and ensure the future success of Wayve by incubating and investing in new ideas that have the potential to become game-changing technological advances for the company.

What this team is solving:

Our focus is on algorithmic advances in reinforcement learning, efficient model architectures, and multimodal representation learning — all aimed at expanding the capabilities of Wayve's AI Driver.

We're not just optimising for performance — we're pushing the frontiers of what driving intelligence can understand, adapt to, and explain. The team is designing AI models that are more efficient, more robust to rare events and real-world complexity, and more interpretable.

By advancing core features like temporal abstraction, latent action modeling, and data-adaptive learning, our goal is to deliver an AI driver that learns faster, adapts better, and earns trust through transparency.

Where you’ll have impact

As a Research Manager in the Science department in the On-Road Innovation team, you will play a pivotal role at the intersection of technical contribution and team development. You’ll split your time between hands-on ML research and leading a high-performing interdisciplinary team of applied scientists and ML engineers (up to 6 reports), all working in a tightly aligned research program.

Your team will work across multimodal learning, latent action spaces, planning, reinforcement learning, world modelling, and efficient AI. You’ll contribute directly to these efforts, guiding experimental design, implementation, and analysis. In parallel, you'll help others make faster progress through technical mentoring, peer feedback, and shaping a culture of executional and scientific excellence.

This is a career-track manager position for a high-agency scientist who is excited to grow as a leader — not by stepping away from the technical frontier, but by staying at the core of it. You’ll help scale both science and people, driving forward new capabilities in autonomy while enabling others to thrive and innovate alongside you.

Key responsibilities:

Technical Contributions & Leadership

• 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 (~50% of time).
• Disrupt thoughtfully – Challenge assumptions, ask sharp questions, and champion bold ideas that push us beyond incremental gains and toward breakthrough advances

Team Management & Cross-Functional Execution

• 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. 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.
• Align and connect – Build bridges with Engineering, Product, and other Science teams. Represent the team in cross-functional settings, ensuring clarity on ownership, scope, and technical interfaces. Communicate priorities clearly, manage interfaces thoughtfully, and ensure your team’s efforts align with company goals. Collaborate with other teams to ensure seamless integration of ML models into our Wayve models and platforms.
• Architect teams – Hire and grow a world-class team of applied scientists and ML engineers. Build inclusive structures, foster belonging, and scale high-performance operating models.
• Level up – Support the team’s growth through feedback, coaching, and peer mentorship. Invest in developing people while modelling excellence in your own technical work. Foster a culture of ownership, technical excellence, and collaboration.
• Champion change – Lead through ambiguity. Balance structure and adaptability to help your team navigate evolving priorities, novel research, and complex organisational change.

About you  

In order to set you up for success as a Research Manager at Wayve, we’re looking for the following skills and experience.  

Essential 

• 5+ years of experience in ML engineering or applied research roles.
• 2+ years of people management experience, including direct reports and cross-functional project ownership.
• You have a demonstrated track record of impactful technical work in one or more of: multimodal learning, RL, generative models, latent action modeling, efficient AI, or planning.
• You have experience building large-scale ML infrastructure and working with high-dimensional temporal data (e.g., video, multi sensor).
• You have a deep understanding of the full lifecycle of ML research and deployment.
• You have strong Python and PyTorch engineering fundamentals, and experience building research-grade production tools.
• You have a track record of delivering ML systems that translate into product or platform wins.
• You are a clear, thoughtful communicator who can mentor effectively and inspire confidence.
• You balance technical depth with people leadership. You know when to lead from the front and when to empower your team.
• You embrace ambiguity and help your team make sense of it, keeping clarity and momentum through uncertainty.
• You are a systems thinker who builds scalable team structures, rituals and career development frameworks.

Nice to have 

• Experience in AVs, robotics, simulation, or other embodied AI domains.
• Familiarity with model personalisation, distillation, or edge model optimisation.
• Experience curating large-scale datasets for model training or evaluation.
• Publications in top ML conferences (e.g., NeurIPS, ICLR, ICML) or contributions to open-source ML tooling
• Previous experience in startup-like or high-ambiguity environments.

This role might not be for you if

• You’re at your best when solving complex technical problems hands-on, rather than leading through others via mentoring and team support.
• You are mainly managing teams and no longer hands-on in technical projects, and have no desire to get in the weeds/code again.
• You aren’t comfortable working on high-ambiguity, research-adjacent projects with moving targets.
• You haven’t yet managed a team with direct reports through full-cycle planning, delivery, and feedback loops.