LLM Fine-Tuning Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build scalable distributed training pipelines
- Construct curate and quality assure instruction and preference datasets
- Design fine tuning experiments for large language models
- Document training methodology results and decisions
- Implement evaluation suites for benchmarks and human evaluation
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate GPU cluster training jobs and recover from failures
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter-Tuning | Attention Optimization | Automated benchmarking | DPO | Dataset curation | Distributed Training | Evaluation methodology | FSDP | Fine Tuning | GPU clusters | Human evaluation | Hyperparameter Optimization | Hyperparameter Tuning | Lineage Tracking | LoRA | Mixed Precision | Model Lineage Tracking | Model lineage | Pipeline parallelism | Policy Evaluation | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Safety evaluation | Sequence Packing | Supervised Learning | Transformers | Zero
Education
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