Principal Machine Learning Engineer (Scenario Technology)

Where you will have an impact

We’re revolutionizing the way autonomous vehicles learn and operate, and the Scenario Technology and Coverage team plays a pivotal role in that mission by building scalable, data-driven scenarios for evaluating and validating self-driving models. As a Principal ML Engineer on the Scenario Technology and Coverage team, you'll lead the design and implementation of scalable ML-based tools that enhance our scenario pipeline—from data mining and scenario generation to simulation-based evaluation. Your work will be critical in ensuring that our driving models are thoroughly tested and optimized across diverse and realistic conditions.

Challenges you will own: 

• Lead the development of robust ML models and augmentation techniques that power our scenario generation and validation systems.
• Architect scalable systems for mining, curating, and synthesizing data to fuel scenario databases, using both real-world and synthetic data.
• Collaborate with cross-functional teams, including Simulation and Science, to integrate state-of-the-art technologies into our scenario pipelines.
• Drive innovation in scenario coverage, focusing on both closed-loop and open-loop evaluations.
• Own end-to-end technical solutions: from translating product requirements into engineering tasks to delivering high-impact ML models and infrastructure improvements.
• Identify and address infrastructure gaps, leading initiatives to enhance our foundational tools.
• Guide the team in navigating complex technical challenges and setting the technical direction in scenario technology.
• Mentor engineers across teams, fostering growth and innovation in scenario-driven ML engineering.

About you  

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

Essential 

• M.S. or Ph.D. in Computer Science, Machine Learning, or related fields, with 10+ years of industry experience.
• Proven experience in building, deploying, and scaling production-level ML solutions.
• Deep understanding of ML fundamentals with expertise in one or more areas: Deep Learning, Computer Vision, Traditional ML, or Large Language Models.
• Proficiency in Python, with a strong focus on clean, well-structured, and testable code.
• Experience leading and executing large-scale, cross-functional projects.
• 7+ years of experience in ML engineering and 4+ years in production deployments.
• Hands-on experience with data pipelines, SQL, and managing large-scale databases.

Desirable 

• • 3+ years of experience in simulation, perception, or autonomous driving technologies.
  • Familiarity with training and evaluating autonomous vehicle models.
  • Industry experience in autonomous vehicles, robotics, transportation, or computer vision.

What we offer you

• The chance to be part of a truly mission driven organisation and an opportunity to shape the future of autonomous driving.  Unlike our competitors, Wayve is still relatively small and nimble, giving you the chance to make a huge impact
• Competitive compensation
• Fully employer-covered medical, dental and vision insurance!
• Further benefits such as catered lunch, yummy snacks, and variety of drinks, life insurance, employer contributed retirement account, therapy, yoga, office-wide socials and much more.
• A dynamic and fast-paced work environment in which you will grow every day - learning on the job, from the brightest minds in our space, and with support for more formal learning opportunities too
• A culture that is ego-free, respectful and welcoming (of you and your dog) - we even eat lunch together every day
• This is a full-time role based in our office in California. 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 also operate core working hours so you can be where you need to be for family and loved ones too.  Teams determine the routines that work best for them.

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