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The Staff Machine Learning Engineer builds and fine‑tunes computer‑vision and scene‑understanding models for offline performance measurement of autonomous vehicles, leveraging Python, PyTorch, transformer‑based vision models and large foundation models, and benchmarks their accuracy across platforms and conditions.
Develops 3D foundation models and world models for autonomous driving systems, focusing on geometric vision, multi-view geometry, and sensor fusion (LiDAR, radar, camera) to enable vehicles to perceive and navigate complex environments.
Build and refine computer vision models to measure autonomous driving performance offline, adapting foundation models for scene understanding and benchmarking accuracy across conditions.
Software Engineer on Wayve's AI Libraries team building Python libraries, tools, and platforms that enable ML engineers to train, evaluate, and scale autonomous driving models across large GPU clusters.
The Machine Learning Engineer will build and scale generative world models to create synthetic data for autonomous driving systems. This role involves collaborating with researchers to post-train models, manage large-scale GPU inference pipelines, and integrate synthetic data into the training stack for driving models.
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