LLM Fine-Tuning Engineer
Tasks
- Build instruction tuning and preference datasets
- Collaborate with product research and platform teams
- Create scalable distributed training pipelines
- 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
- Implement safety refusal and policy evaluations
- Manage model artifacts and ensure reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate GPU cluster training jobs and recover from failures
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production training recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter | Benchmarking | DPO | Dataset curation | DeepSpeed ZeRO | Distributed Training | Efficient Attention | Experiment tracking | FSDP | GPU clusters | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Mixed-precision training | Model Evaluation | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Safety evaluation | Sequence Packing | Transformers
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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