Applied Scientist / Machine Learning Engineer

Join our AI Platform organization as an Applied Scientist or Machine Learning Engineer. You will own the quality and intelligence of the data engine that powers our foundation models. This dual-track role offers opportunities in data curation, data enrichment, and foundation model evaluation. You will work with large-scale fleet data, build high-quality enrichments, and contribute to the wider foundation-model stack. Enjoy benefits such as private healthcare, paid time off, mental health resources, community and socials, competitive compensation, and learning and development opportunities.

• Data curation: Mine world-scale fleet data for rare, long-tail, and safety-critical moments that move the model.
• Data enrichment: Turn raw driving experience into high-signal training data through (semi-)automated enrichment, labeling, and data quality at scale.
• Foundation model evaluation: Define how we know a driving foundation model is genuinely getting better, offline and in closed loop.
• Fluency in Python and a modern deep-learning framework (PyTorch or similar), and comfort working with large, messy, real-world datasets
• Hands-on strength in one or more of: data curation, foundation model training, large-scale data wrangling, and foundation-model evaluation (for example, evaluation of LLMs or similar large models)
• A Masters with around 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 (required)
• Strong ML and software fundamentals, and a track record of taking ML from research into production systems that run at scale
• Experience with large-scale data and/or large neural networks, and the judgment to know which experiments and which data actually matter
• Foundation models, VLMs, world models, diffusion or autoregressive generative models, or reinforcement learning and reward modeling
• Autonomous driving, robotics, or other embodied-AI domains
• 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
• Closed-loop or simulation-based evaluation, and safety-critical ML
• Publications at top ML, CV, or robotics venues (NeurIPS, ICML, ICLR, CVPR, CoRL, RSS)
• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply