Machine Learning Engineer

As an ML Engineer within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration.

You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.

Key Responsibilities:

Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.

Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.

Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.

Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.

Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.

Collaborate cross-functionally across various teams to ensure integration and iteration velocity.

Mentor senior engineers and shape the long-term technical direction across Autonomy.

About You 

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

Essential 

Extensive and proven track record of shipping deep learning systems to production.

Expert in deep learning (esp. sequential models, control, planning, or perception).

Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.

Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.

Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.

Desirable 

Prior work in autonomous driving, imitation learning, or trajectory prediction.

Familiarity with personalization, human behavior modeling, or driver intent inference.

Experience integrating ML systems into production hardware or multi-agent simulation.

This is a full-time role based in our office in Leonberg. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team.  

#LI-KM1

Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. 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 

For US candidates only, please visit E-Verify Notice and Participation and Right to Work

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.

 

 

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