Lead Machine Learning Engineering, (Hybrid)
Seattle, Washington, United States of America
R
USD 197K-249K Senior-level Full Time
Tasks
- Automate data generation labeling scoring and evaluation
- Build and improve data and machine learning systems for LLMs
- Collaborate on dataset requirements for fine tuning preference learning and agent development
- Create synthetic datasets
- Design and build scalable data pipelines
- Design experiments linking dataset composition to model performance
- Develop human in the loop labeling workflows
- Implement task generation and quality control for labeling
- Integrate feedback into labeling workflows
- Maintain ML and LLM data lifecycle pipelines
- Measure and mitigate dataset failure modes
- Provide technical direction on infrastructure compute and storage
Perks/Benefits
Skills/Tech-stack
Apache Beam | C++ | Data Processing | Data labeling | Deep learning | Distributed Systems | Distributed data | Distributed data processing | Go | Human-in-the-loop | LLM | Machine Learning | PyTorch | Python | Ray | Spark | Synthetic data | TensorFlow | The Loop
Education
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