LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines
- Collaborate with product and research teams
- Construct curate and QA instruction tuning and preference datasets
- Design evaluation suites with automated and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement parameter-efficient fine-tuning methods
- Implement safety and policy evaluations
- Manage model artifacts and experiment reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate training jobs on GPU clusters
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research advances into production recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapters | Benchmarks | DPO | Distributed Training | Efficient Attention | Evaluation methodology | FSDP | Fine Tuning | GPU Computing | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Red Teaming | Reproducibility | Safety evaluation | Sequence Packing | Supervised Learning | Transformer | Zero
Education
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