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 Application Software Teams

Our Application Software team pushes the frontier of model-based autonomous driving, improving core driving performance and developing intelligent features such as personalisation, comfort and collaboration.

As a Machine Learning Engineer, you'll lead critical initiatives to design and deliver ML-driven behaviours that scale from assisted to autonomous driving. Your work will span model architecture, data pipelines, evaluation frameworks and real-world deployment.

You'll work closely with AI Platform, Simulation, Robot Software and Model Release teams to build systems that are performant, adaptable and ready for production, while helping shape the long-term technical direction across Autonomy.

Your day-to-day

• Develop and improve end-to-end driving models, advancing their performance, robustness and generalisation.
• Lead technical initiatives in personalised and collaborative driving, including behaviour conditioning, comfort tuning and alignment with user preferences.
• Build evaluation pipelines and metrics to assess open-loop and closed-loop driving performance and product readiness.
• Curate and mine real-world and synthetic data to improve scenario diversity, coverage and feature-specific development.
• Collaborate across engineering teams to integrate models, accelerate iteration and support real-world deployment.
• Mentor senior engineers, drive technical alignment and influence architecture, training and deployment decisions.

What you'll be working on

• End-to-end driving intelligence: Advance the models that underpin assisted and autonomous driving, connecting model development with real-world performance.
• Personalisation, comfort and collaboration: Develop ML-driven behaviours that adapt to user preferences and support comfortable, collaborative driving.
• Evaluation and product readiness: Create frameworks that measure driving quality, robustness and feature performance in both open-loop and closed-loop settings.
• Data-driven model improvement: Use real-world and synthetic data to uncover gaps, broaden scenario coverage and support targeted feature development.
• Production-scale learning systems: Shape model architectures, training methodologies and deployment pathways, working across AI Platform, Simulation, Robot Software and Model Release.
• Technical leadership: Lead cross-team initiatives and help define the long-term technical direction of our autonomy capabilities.

You should apply if

• You have an extensive, proven track record of shipping deep learning systems to production.
• You bring deep expertise in deep learning, particularly sequential models, control, planning or perception.
• You're proficient in Python, experienced with relevant languages such as C++ or CUDA, and confident using ML frameworks, especially PyTorch.
• You have a strong foundation in software engineering practices and experience building reliable, maintainable ML systems.
• You have worked with real-time systems or robotics, ideally including simulation-in-the-loop or vehicle-in-the-loop components.
• You can lead technical initiatives across teams, build alignment and mentor engineers.

It would also be great if you bring:

• Experience in autonomous driving, imitation learning or trajectory prediction.
• Familiarity with personalisation, human behaviour modelling or driver intent inference.
• Experience integrating ML systems into production hardware or multi-agent simulation.

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.

How we work - Locations & Flexible Working:

This is a full-time role based in Sunnyvale, CA, or Detroit, MI, with a hybrid working model.

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 minutes)
• Competency Interviews: Python programming and Pytorch debugging (2 hours)
• Deep-dive technical interviews System Design, ML (1 hour)
• Final interview: mission & values alignment (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.