Principal Research Scientist – Multimodal Reasoning & Embodied AI

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 Labs Team

Wayve Labs develops the next generation of learning capabilities for embodied AI, using autonomous driving as a demanding real-world testbed. Within FORGE, our Language & Reasoning team explores how LLMs, VLMs and VLAs can connect world knowledge, grounded reasoning, memory and planning to physical action. We work across model architectures, training and post-training, with the goal of helping driving agents generalise to new environments, follow goals and constraints, and handle rare situations. The team collaborates across London and Sunnyvale, with opportunities to publish fundamental research.

🧠 Your day-to-day

You will set and lead an ambitious research agenda at the intersection of LLMs, VLMs, VLAs and embodied AI, asking how agents can use world knowledge, visual understanding, memory and reasoning to make better decisions in the physical world.

You will remain hands-on: developing new architectures and learning methods, training and evaluating large-scale models, and testing whether better reasoning leads to better behaviour in challenging driving scenarios.

As a Principal Research Scientist, you will guide the technical direction of the team, mentor researchers, build strong collaborations with Engineering, and turn original ideas into rigorous evidence, influential publications and capabilities that shape Wayve's next generation of AI.

🧩 What you’ll be working on:

• Advance multimodal foundation models: Research and develop LLMs, VLMs and VLAs that integrate visual understanding, language, reasoning and action for embodied decision-making.

• Develop reasoning and planning capabilities: Explore structured and latent reasoning, long-horizon planning, temporal understanding, memory, self-reflection and test-time computation for physically grounded agents.

• Push the boundaries of model training: Develop scalable training and post-training methods, including supervised fine-tuning, reinforcement learning, reward modelling, distillation and learning from human or AI-generated feedback.

• Build steerable and adaptable policies: Enable embodied agents to understand instructions, follow goals and preferences, reason about constraints and generalise to novel environments and situations.

• Explore agentic and hierarchical architectures: Investigate how deliberative reasoning, memory, external tools and fast reactive policies can work together, balancing intelligence, reliability and real-time constraints.

• Bridge reasoning and action: Develop approaches that turn semantic understanding and reasoning into meaningful improvements in physical-world behaviour, rather than treating language generation as an end in itself.

• Drive research from idea to evidence: Design rigorous experiments, build prototypes, establish challenging evaluations and demonstrate improvements in real-world capabilities.

• Shape the research direction: Work closely with researchers across foundation models, representation learning, world modelling and reinforcement learning. Contribute original ideas, mentor others and help define the next generation of embodied AI systems.

🙌 You should apply if:

• A strong research track record in deep learning, particularly in one or more of LLMs, VLMs, VLAs, multimodal foundation models or embodied AI.

• Deep technical understanding of modern foundation model architectures, transformer-based models, multimodal representation learning and generative modelling.

• Hands-on experience developing, training, fine-tuning or post-training large-scale models, with a solid understanding of the challenges involved.

• Strong knowledge of modern learning methods, such as instruction tuning, reinforcement learning, preference optimisation, reward modelling or reasoning-oriented post-training.

• Excellent understanding of machine learning fundamentals, combined with the ability to challenge existing approaches and develop novel research ideas.

• Strong practical skills in Python, PyTorch and large-scale machine learning experimentation.

• Demonstrated ability to independently lead ambitious research projects, from identifying important problems to developing solutions and validating their impact.

• A PhD in Machine Learning, Computer Science, Robotics or a related field, or equivalent research experience, with a strong publication record or evidence of impactful research contributions.

🌱 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 Wayve:

🚀 Wayve 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 Working:

Our 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 Process:

Our process is clear and respectful of your time:

• Initial call / recruiter screen (30 mins)

• Competency Interviews (Hiring Manager interview; 1 hour)

• Deep-dive technical interviews ( PyTorch debugging, ML Breadth, ML Research, ML System Design; 3 hours total)

• Final interview: Mission & values alignment (Director / VP interview; 45 minutes)

We’ll always explain the format and work around your availability.

What’s in it for you (Location dependant):

💰 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

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.
At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

For more information visit Careers at Wayve. To learn more about what drives us, visit Values at Wayve
DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.