Applied Scientist / Machine Learning Engineer

Stack

Responsibilities

• ▹Mine world-scale fleet data for rare, long-tail, and safety-critical events using active learning and embedding-based retrieval
• ▹Design repeatable data curation across cities, sensor rigs, and embodiments
• ▹Build automated enrichment and labeling pipelines and ensure data quality at scale
• ▹Build and fine-tune large-scale pretrained models, running smaller experiments to derisk ideas
• ▹Contribute to the LINGO vision-language-action model line
• ▹Design rigorous offline and closed-loop evaluation metrics and benchmarks
• ▹Use the GAIA world model to probe counterfactual scenarios safely

Requirements

• ▹MSc with ~6+ years of relevant experience, or PhD with 2+ years, in CS, ML, robotics, mathematics, or a related field
• ▹Strong ML and software fundamentals with a track record of taking ML from research into production at scale
• ▹Hands-on experience in data curation, foundation model training, large-scale data wrangling, or foundation-model evaluation
• ▹Experience with large-scale data and/or large neural networks
• ▹Fluency in Python and a modern deep-learning framework (PyTorch or similar)

Nice to have

• ▹Background in autonomous driving, robotics, or other embodied-AI domains
• ▹Experience with foundation models, VLMs, world models, diffusion/autoregressive models, or RL and reward modeling
• ▹Large-scale data infrastructure experience (vector search, Ray Data/Daft/Spark, Lance/Iceberg, annotation tooling)
• ▹Closed-loop or simulation-based evaluation, safety-critical ML
• ▹Publications at top ML/CV/robotics venues (NeurIPS, ICML, ICLR, CVPR, CoRL, RSS)

Soft skills

What we offer

• ▹Competitive equity package
• ▹Hybrid working policy combining office and home
• ▹Estimated salary range $311,850–$370,000

About the company

Wayve builds embodied AI for the physical world, starting with autonomous driving. Instead of hand-engineered modular stacks, it pioneered AV2.0 — a single end-to-end neural network that learns to drive from raw sensor data. Its GAIA and LINGO foundation model families power vehicles that have driven zero-shot across hundreds of cities, and it is scaling deployment with automaker partners including Nissan, Stellantis, and Uber.