Applied Scientist / Machine Learning Engineer

Wayve•Sunnyvale, CA

4d•Hybrid

About The Position

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

Requirements

A Masters 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 taking ML from research into production systems that run at scale.
Hands-on strength in one or more of: 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 judgment to know which experiments and data actually matter.
Fluency in Python and a modern deep-learning framework (PyTorch or similar).
Comfort working with large, messy, real-world datasets.

Nice To Haves

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: 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 and establish repeatable curation processes across different cities, sensor rigs, and embodiments.
Build high-quality enrichments through semi-automated pipelines and data quality at scale, which will be used by teams across the company.
Build and fine-tune large-scale pretrained models, and conduct smaller-scale experiments to test and derisk ideas.
Contribute to building advanced embodied Vision-Language Models (VLMs) / Vision-Language-Action (VLA) models for driving, focusing on multimodal perception, reasoning, language, and action.
Design rigorous offline and closed-loop evaluation metrics and benchmarks that correlate with real on-road behavior and safety, with deliberate coverage of 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, including generative world models (GAIA), policy learning, reinforcement learning, and reward modeling, as needed.