Applied Scientist / Machine Learning Engineer
Core
Curating, enriching, and evaluating world-scale fleet data to improve end-to-end autonomous driving foundation models (GAIA, LINGO) for deployment with automakers.
Role type
Senior Applied Scientist / Staff Machine Learning Engineer (Data Flywheel)
Builds
High-signal training datasets, automated enrichment pipelines, and rigorous evaluation frameworks for embodied AI models.
Domain
Autonomous driving, Embodied AI, Foundation Models
Deliverable
production ML models
Required skills
Data curation, foundation model training, large-scale data wrangling, foundation-model evaluation, Python, PyTorch, active learning, embedding-based retrieval, data quality at scale
Preferred skills
Autonomous driving, robotics, VLMs, world models, reinforcement learning, reward modeling, distributed data processing (Ray, Spark), vector search (Milvus, Turbopuffer), simulation-based evaluation
Technologies
PyTorch, Ray Data, Daft, Spark, Lance, Iceberg, Milvus, Turbopuffer
Responsibilities
Mine fleet data for rare/safety-critical events using active learning and smart sampling; define optimal data mixes and balance for model improvement; build automated enrichment and labeling pipelines; fine-tune large-scale pretrained models and run small-scale experiments; push multimodal perception and reasoning for the LINGO VLA line; design offline and closed-loop evaluation metrics correlating with real-world safety; use world-model-based evaluation (GAIA) for counterfactual scenario probing.
Seniority
Senior, hands-on IC / Staff, strategy & mentorship
