LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines for distributed training frameworks
- Collaborate with product research and engineering teams to align training roadmaps
- Construct curate and validate instruction and preference datasets
- Design fine tuning experiments for large language models
- Develop evaluation suites using automated and human evaluations
- Document training methodology and results
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate training jobs on GPU clusters and recover training state
- Optimize training throughput with mixed precision sequence packing and efficient attention
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter methods | Attention Mechanisms | Benchmarking | DPO | Data Generation | Dataset Distillation | FSDP | GPU clusters | Human evaluation | LoRA | Mixed Precision | Model Evaluation | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Sequence Packing | Supervised Learning | Synthetic Data Generation | Synthetic data | Transformers | Zero
Education
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