Electric-Vehicle
Location: CA North
About the job
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 Wayve
Founded in 2017, Wayve leads in Embodied AI technology, developing advanced software and foundation models that allow vehicles to perceive, navigate, and understand complex real-world environments. Our hardware-agnostic, mapless AI systems accelerate the transition from assisted to fully automated driving, improving usability and safety across autonomous platforms.
We tackle challenging problems in a fast-paced, collaborative environment, leaning into uncertainty to deliver innovative solutions. At Wayve, your contributions matter — we embrace diversity, value new perspectives, and cultivate an inclusive culture where everyone can thrive.
The Role:
We are seeking Machine Learning Engineers to join our Training Tech team. In this position, you will focus on optimizing large-scale model training to allow Wayve to scale AI models efficiently and train larger models faster. You’ll help improve the performance of GPU clusters, implement observability tools, and collaborate closely with research teams to integrate optimizations into production workflows.
This role is hands-on, high-impact, and critical to pushing the next generation of Wayve’s AI systems.
Key Responsibilities:
Profile and analyse large-scale training jobs to identify performance bottlenecks (e.g., using NVIDIA Nsight Systems).
Design and implement training efficiency improvements, including tensor parallelism, model compilation, and mixed-precision computation.
Build observability tools to track GPU utilization and training efficiency over time.
Partner with research teams to integrate performance optimizations and promote a culture of high-performance ML.
Continuously monitor, benchmark, and report training job performance to maintain transparency and drive improvements.
About You
Essential Qualifications:
Experience optimizing large-scale training workloads on GPU clusters.
Background in platform or infrastructure teams and experience collaborating with research teams.
Ability to track, benchmark, and report performance metrics in an accessible way.
Strong Python development skills, writing well-structured, testable, and maintainable code.
BS or MS in Machine Learning, Computer Science, Engineering, or a related technical discipline, or equivalent practical experience.
Desirable Skills:
Experience with concurrent, parallel, and distributed computing.
Familiarity with NVIDIA Nsight Systems and GPU kernel implementation.
Deep understanding of computing fundamentals that affect performance, security, and reliability.
Why Join Wayve
Work at the cutting edge of AI for autonomous vehicles.
Make a tangible impact on model training efficiency and performance at scale.
Hybrid work model combining collaborative in-office time and remote flexibility.
Core working hours with flexibility to set a schedule that works for you and your team.
Join a culture that values diversity, inclusion, and continuous learning.
We understand that not every candidate will meet all qualifications. If you’re passionate about self-driving technology and performance engineering, 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.
Electric-Vehicle
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