LLM Fine-Tuning Engineer
Tasks
- Build dataset construction and quality assurance processes
- Build scalable training pipelines with distributed training frameworks
- Design evaluation suites with automated benchmarks 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 evaluation
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning best practices and responsible deployment
- Operate training jobs on GPU clusters and recover training state
- Optimize training throughput with mixed precision and efficient attention
- Stay current with LLM research and productionize fine tuning recipes
- Tune hyperparameters and training stability strategies
Perks/Benefits
Skills/Tech-stack
Adapters | Attention Mechanisms | DPO | Dataset curation | Evaluation benchmarks | FSDP | GPU clusters | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Model Reproducibility | Optimizer Configuration | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Sequence Packing | Supervised Learning | Transformers | Zero
Education
Roles
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