LLM Fine-Tuning Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build distributed training pipelines
- Collaborate with product and research teams
- Construct and curate instruction datasets
- Design evaluation suites with automated and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology and results
- Execute supervised training pipelines
- Implement preference optimization methods
- Implement safety and policy evaluation
- Manage model artifacts and experiment reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate GPU cluster training jobs
- Optimize training throughput with mixed precision
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter-Tuning | Automated Benchmarks | Data Curation | Direct Preference Optimization | Distributed Training | Evaluation methodology | FSDP | GPU Cluster | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Transformer Models | Zero
Education
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