Applied Scientist / Machine Learning Engineer
Tasks
- Build and fine tune pretrained models
- Build data enrichment and labeling pipelines
- Create metrics and benchmarks for safety scenarios
- Curate and balance training datasets
- Design offline and closed loop evaluation
- Evaluate counterfactual scenarios with world models
- Improve data quality at scale
- Mine fleet data for rare events
- Run experiments to evaluate ideas
- Support generative world models policy learning reinforcement learning and reward modeling
Perks/Benefits
Skills/Tech-stack
Action models | Active Learning | Data Curation | Data Deduplication | Data Processing | Data Wrangling | Data labeling | Deep learning | Distributed data | Distributed data processing | Embedding-based retrieval | Evaluation metrics | Generative Models | Iceberg | Lakehouse | Language Models | Machine Learning | Milvus | Multimodal Learning | PyTorch | Python | Ray Data | Reinforcement Learning | Reward Modeling | Sampling | Spark | Vector Search | Vision Language Models | Vision-Language-Action Models | Vision-language | Vision-language-action | World Models
Education
Regions
Countries
States
Cities
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