LLM Fine-Tuning Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build distributed training pipelines
- Collaborate with cross-functional teams
- Construct and curate training datasets
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement evaluation suites
- Implement safety and policy evaluations
- Manage model artifacts and reproducibility
- Mentor engineers
- Operate GPU cluster training jobs
- Optimize training throughput
- Translate LLM research into production workflows
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapters | Attention Optimization | Benchmarking | Dataset curation | Direct Preference Optimization | Distributed Training | Efficient Fine Tuning | Evaluation | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | Lineage Tracking | LoRA | Mixed Precision | Model Lineage Tracking | Model Reproducibility | Model lineage | Parameter efficient fine-tuning | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Transformer | Zero
Education
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