LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines using distributed training frameworks
- Construct curate and QA instruction tuning and preference datasets
- Design evaluation suites with automated benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology results and decisions
- Implement parameter efficient fine tuning methods such as LoRA and QLoRA
- Implement safety refusal and policy evaluation
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning best practices and responsible deployment
- Operate large scale training jobs on GPU clusters and recover from failures
- Optimize training throughput using mixed precision sequence packing and efficient attention
- Tune hyperparameters and optimizer configurations for training stability
Perks/Benefits
Skills/Tech-stack
DPO | Efficient Attention | Evaluation | FSDP | GPU clusters | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Model Reproducibility | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Safety evaluation | Sequence Packing | Transformer | Zero
Education
Roles
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