Applied Scientist / Machine Learning Engineer

Wayve•Sunnyvale, CA

5d•$311,850 - $370,000•Hybrid

About The Position

Wayve is seeking an Applied Scientist or Machine Learning Engineer to join the AI Platform organization, specifically focusing on the data flywheel that powers their foundation models. This role is crucial for intelligently curating, enriching, and evaluating real-world driving data to improve model performance and generalization. The position is open at either the Senior (TC3) or Staff/Tech Lead (TC4) level, with opportunities to specialize in data curation, data enrichment, or foundation model evaluation. The role also involves working with the broader foundation-model stack, including vision-language-action and vision-language models, world modeling, policy learning, reinforcement learning, and reward modeling.

Requirements

A Master's degree with approximately 6 or more years of relevant experience, or a PhD with 2 or more years, in computer science, machine learning, robotics, mathematics, or a related field.
Strong ML and software fundamentals.
A track record of successfully transitioning ML from research into production systems that operate at scale.
Hands-on expertise in at least one of the following: data curation, foundation model training, large-scale data wrangling, and foundation-model evaluation (e.g., evaluation of LLMs or similar large models).
Experience with large-scale data and/or large neural networks, and the ability to discern which experiments and data are most impactful.
Fluency in Python and a modern deep-learning framework (PyTorch or similar).
Comfort working with large, complex, real-world datasets.

Nice To Haves

Experience in autonomous driving, robotics, or other embodied-AI domains.
Experience with foundation models, VLMs, world models, diffusion or autoregressive generative models, or reinforcement learning and reward modeling.
Experience with large-scale data infrastructure, including embedding and vector search (e.g., turbopuffer, Milvus), distributed data processing (Ray Data, Daft, Spark), lakehouse formats (Lance, Iceberg), or annotation tooling.
Experience with closed-loop or simulation-based evaluation, and safety-critical ML.
Publications at top ML, CV, or robotics venues (NeurIPS, ICML, ICLR, CVPR, CoRL, RSS).

Responsibilities

Mine world-scale fleet data for rare, long-tail, and safety-critical events using active learning, smart sampling, and embedding-based retrieval and dedup.
Determine what constitutes a good training dataset, identifying which data, mix, and balance effectively improve the model, and establishing repeatable curation processes across different cities, sensor rigs, and embodiments.
Build high-quality enrichments that are depended upon by teams across the company, utilizing (semi-)automated enrichment and labeling pipelines and ensuring data quality at scale.
Build and fine-tune large-scale pretrained models, and conduct smaller-scale experiments to test and derisk ideas before committing significant computational resources.
Contribute to building the world's best embodied VLM/VLA for driving (the LINGO line), advancing multimodal perception, reasoning, language, and action.
Design rigorous offline and closed-loop evaluation methods, including metrics and benchmarks that correlate with real on-road behavior and safety, with a focus on rare and safety-critical scenarios.
Utilize world-model-based evaluation (GAIA) to safely, repeatably, and at scale probe counterfactual 'what if' scenarios.
Contribute to the wider foundation-model stack as needed, including generative world models (GAIA), policy learning, reinforcement learning, and reward modeling.