Applied Scientist / Machine Learning Engineer

Job description

Job 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.
• Figure out what makes a good training dataset: which data, mix, and balance actually move the model, and turn that into repeatable curation across cities, sensor rigs, and embodiments.
• Build high-quality enrichments that teams across the company depend on, through (semi-)automated enrichment and labeling pipelines and data quality at scale.
• Build and fine-tune large-scale pretrained models, and run smaller-scale experiments to test and derisk ideas before committing serious compute.
• Help build the best embodied VLM / VLA in the world for driving (the LINGO line): push multimodal perception, reasoning, language, and action.
• Design rigorous offline and closed-loop evaluation: metrics and benchmarks that correlate with real on-road behaviour and safety, with deliberate coverage of rare and safety-critical scenarios.
• Use world-model-based evaluation (GAIA) to probe counterfactual “what if” scenarios safely, repeatably, and at scale.
• Contribute across the wider foundation-model stack as the work demands: generative world models (GAIA), policy learning, reinforcement learning, and reward modeling.