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This is a rare opportunity to join the small but high-leverage engineering team powering Wayve’s foundation model—at the heart of our end-to-end autonomous driving. Embedded within the Science group, you’ll build the infrastructure that enables researchers to iterate faster, train at scale, and ship smarter models. If you thrive at the intersection of research and engineering, and love building systems that accelerate discovery, this is your chance to make a defining impact on one of the most ambitious AI challenges in the world.
Job Responsibility:
Design and scale infrastructure for data ingestion, filtering, and curation of multi-modal embodied data
Build robust, efficient training, evaluation, and inference pipelines to support foundation model development
Partner closely with scientists and MLEs to accelerate experimentation and unblock research
Improve ML systems performance, scalability, and automation across the stack
Act as a cross-functional force multiplier—connecting Science, Software, and Data teams through well-designed tooling and systems
Requirements:
Strong software engineering skills with experience building and maintaining distributed systems, data pipelines, or backend platforms at scale
Experience developing infrastructure that supports machine learning workflows—such as training orchestration, evaluation tooling, or inference systems
Comfort working closely with research or ML teams to understand their iteration needs and build systems that accelerate them
Familiarity with technologies like Flyte, Ray, Spark, Airflow, or Kubernetes, and an understanding of how to use them to scale data and compute
Ownership mindset with the ability to identify bottlenecks, operate across team boundaries, and “get stuff done” in ambiguous, fast-moving environments
Nice to have:
Experience working with large-scale multi-modal datasets (e.g. video, LiDAR, radar, language) and designing systems for ingestion and filtering
Prior experience in a foundation model or autonomy-focused team, especially in an infrastructure or ML platform role
Contributions to open-source ML or infra projects (e.g. Flyte, Ray, Dask, MLFlow) or experience with evaluation tooling at scale
Demonstrated technical leadership—whether through driving cross-functional projects, mentoring others, or setting architectural direction
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