Staff Machine Learning Engineer

Where you will have an impact

We are seeking an experienced Machine Learning Engineer to join our foundational Vancouver office and help lead engineering efforts within our Science team. In this role, you will collaborate closely in our research team to develop, integrate, test, and scale cutting‑edge algorithms, tools, and machine learning solutions for autonomous driving. Your work will directly contribute to enhancing the efficacy and efficiency of our models, and our iteration speed, bringing innovative research ideas to life faster than ever before.

Challenges you will own

You'll be working on some of the world's hardest problems, and able to attack them in new ways. You'll be a technical leader within our diverse, cross‑disciplinary team, helping teach our robots how to drive safely and comfortably in complex real‑world environments. This encompasses many aspects of engineering and optimization across perception, prediction, planning, and control including:

• Designing and implementing end-to-end neural networks and related technologies for autonomous driving applications
• Working closely in our research team to rapidly prototype and iterate on novel machine learning models
• Optimizing models for efficiency and performance, ensuring they can operate effectively in real‑world autonomous driving scenarios
• Developing and maintaining robust machine learning tools as well as infrastructure that accelerates the research and development cycle
• Building metrics and dashboards that give us insight into progress and focus us on solving the right problems
• Continuously evaluating emerging technologies and methodologies to drive innovation within the team
• Leveraging an analytical approach that encompasses both qualitative and quantitative aspects to deliver results and insights
• Ensuring the maintenance of high‑quality training data through the employment of advanced visualization and quantitative analysis techniques
• Effectively communicating data needs within the team to optimize project success and collaboration
• Collaborating with cross‑functional teams to integrate machine learning solutions into our autonomous driving platform
• Contributing to the creation of a collaborative and innovative engineering culture.

What you will bring to Wayve

Essential

• 5+ years of postgraduate industry experience training and deploying large‑scale models on distributed systems
• Strong foundation in machine learning and deep learning
• Experience leading a technical/research agenda aligned with larger organisation or company goals
• Experience bringing research concepts through the full ML development cycle and into production
• Experience working in a metrics and data driven environment
• Ability to stay updated with the latest advancements in model efficiency and apply innovative solutions to enhance existing models or develop new efficient architectures
• Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch, Scikit‑learn, TensorRT, or similar tools for model development
• Strong teamwork and communication skills to collaborate effectively with cross‑functional teams including engineers, researchers, and product managers

Desirable

• Advanced knowledge of model compression, quantisation, distillation, parallelism and/or pruning techniques to reduce model size and improve efficiency
• Understanding of hardware‑specific optimizations for inference, such as optimizing models for GPUs, TPUs, or edge devices.
• PhD or MSc in Computer Science, Machine Learning, Robotics, or a related field

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 and benefits
• 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
• 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