Machine Learning Engineer - AI Synthesis

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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.

About Us:

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

About the Role:

Wayve is seeking experienced Machine Learning Engineers to join our AI Synthesis team
, where you’ll help develop the models, infrastructure, and tooling behind our next-generation synthetic data platform. This team is productising GAIA
, Wayve’s foundation model for synthetic multimodal video, into scalable systems that generate highly realistic, controllable sensor data for evaluating autonomous driving performance.

This is a unique opportunity to work at the intersection of computer vision, generative AI, 3D scene understanding, and robotics
. You’ll contribute to generating synthetic video, lidar, and radar data used for both closed-loop simulation and open-loop validation
, stress‑testing our embodied AI in safety‑critical scenarios.

• Partner with researchers to adapt cutting‑edge generative model architectures for production, ensuring maintainability, performance, and integration with simulation workflows.
• Train and improve models that produce realistic, controllable multimodal sensor data
  , directly supporting evaluation and validation of our autonomy stack.
• Build scalable inference and evaluation pipelines for large generative models working on real and synthetic visual data.
• Apply ML Ops best practices
  : model versioning, reproducibility, evaluation pipelines, and deployment hygiene to ensure models operate reliably in production.
• Develop monitoring tools to track model quality, generation throughput, modality consistency, and coverage of different domains.
• Write clean, modular, and testable code that interfaces effectively with simulation platforms, sensor emulators, and evaluation systems.
• Participate in technical design discussions, code reviews, and architecture planning across model and infrastructure layers.
• Collaborate closely with teams in ML, simulation, cloud, and autonomy to ensure outputs meet real‑world system requirements.
• Promote a culture of engineering rigor and continuous learning through mentorship, shared experiments, documentation, and knowledge sharing.

We’re looking for engineers with complementary strengths in modeling, ML Ops, and ML infrastructure
, and a rigorous approach to working with visual data. Strong software engineering fundamentals, experience with generative models, and knowledge of production ML workflows are essential.

• Hands‑on experience with training and deploying generative models for video or view synthesis.
• Familiarity with ML Ops practices
  , including reproducibility, model versioning, and monitoring.
• Ability to build scalable pipelines for inference and evaluation of large‑scale models.
• Experience collaborating across research, simulation, and cloud infrastructure teams
  .
• Strong coding skills, especially in Python
  , with an emphasis on modular, maintainable, and testable systems.
• A mindset of continuous learning and collaboration
  , with a focus on improving team…