LLM Fine-Tuning Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build dataset construction and quality assurance processes
- Create scalable distributed training pipelines
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement evaluation suites with automated benchmarks and human evaluation
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers and conduct code review design review
- 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 modules | Attention Mechanisms | Benchmarking | DPO | Direct Preference Optimization | Distributed Training | Evaluation | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Optimizer Configuration | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | Red Teaming | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Synthetic data | Zero
Education
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