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MLE - AI Synthesis
Wayve
Software Engineering, Data Science
Sunnyvale, CA, USA
Posted on Sep 17, 2025
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.
In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.
At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.
Make Wayve the experience that defines your career!
The Role
As a Machine Learning Engineer on the AI Synthesis team, you will play a key role in developing the models, infrastructure, and tooling that power Wayve’s next-generation synthetic data platform. Our team is productising GAIA, Wayve’s foundation model for synthetic multimodal video, into scalable tools that generate richly realistic and precisely controllable sensor data to evaluate the performance of our autonomous driving system.
This is a uniquely exciting opportunity to work at the intersection of computer vision, generative AI, 3D scene understanding, and robotics. You’ll contribute to a system that creates synthetic multimodal sensor data, video, lidar, radar, that supports closed-loop simulation and open-loop validation. This synthetic data is used to stress-test and benchmark our embodied AI system in safety-critical scenarios.
We are hiring a cohort of experienced ML Engineers who bring complementary strengths in modeling, ML Ops, and ML infrastructure. The team will focus on training and evolving state-of-the-art generative models for video or view synthesis, and designing scalable pipelines and infrastructure to deploy and monitor these models in production. Across the team, we value strong engineering fundamentals, experience working with visual data, and a rigorous approach to ML Ops practices: model versioning, reproducibility, evaluation, and observability.
You’ll work closely with a growing team of engineers and researchers focused on synthetic data, and collaborate with simulation, autonomy, and cloud infrastructure teams to ensure that what you build delivers real-world value to our autonomy stack.
Challenges you will own
- Collaborate with researchers to bring cutting-edge architectures into production, adapting experimental models for performance, maintainability, and integration into simulation workflows
- Train, and improve generative models that produce realistic and controllable multimodal sensor data, contributing directly to how we evaluate and validate our autonomous driving system
- Build scalable and efficient pipelines for inference and evaluation of large generative models on real and synthetic visual data
- Apply ML Ops best practices: reproducibility, model versioning, evaluation pipelines, and deployment hygiene, to ensure our models can operate reliably in production environments
- Develop tools to monitor, measure, and improve model quality, generation throughput, modality consistency, and domain coverage
- Write clean, modular, and testable code that interfaces well with internal simulation platforms, sensor emulators, and evaluation systems
- Engage in technical design discussions, participate in design reviews, and help shape architecture choices across model and infrastructure layers
- Work closely with teammates across ML, simulation, cloud, and autonomy to ensure our outputs are aligned with real-world system needs and contribute to tangible performance improvements
- Uphold a culture of engineering rigor and continuous learning—through mentoring, code reviews, shared experiments, and thoughtful documentation
About You
Essential
- Proven experience developing and deploying machine learning models, ideally involving visual data such as images, video, or 3D scenes
- Strong fundamentals in machine learning, with the ability to reason about model architecture, training dynamics, data requirements, and failure modes
- Practical experience with ML Ops principles, including model versioning, training reproducibility, CI/CD pipelines for ML, monitoring, and observability
- Ability to write clean, efficient, and maintainable code in Python, with a solid understanding of software engineering best practices
- Familiarity with deep learning frameworks such as PyTorch or TensorFlow, and experience building or modifying training and inference pipelines
- Comfortable working in collaborative, cross-functional teams and contributing to shared goals across engineering, research, and product groups
- Curious, pragmatic, and capable of diving into unfamiliar code, tools, or domains to solve high-impact problems
Desirable
- Experience training or fine-tuning generative models such as diffusion models, GANs, NeRFs, or other video or view synthesis architectures
- Background in vision, perception, or 3D scene understanding, including experience with temporal or multimodal data (e.g., camera, lidar, radar)
- Experience scaling model training and evaluation on large datasets using distributed compute, TPUs, or multi-GPU systems
- Familiarity with physics-informed modeling, domain adaptation, or applying constraints to generative systems
- Contributions to simulation or synthetic data pipelines used in ML model development or evaluation
- Experience developing performant components in C++ or CUDA is a plus, especially in high-throughput or low-latency systems
This is a full-time role based in our office in Sunnyvale. 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 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.
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.