LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines with distributed training frameworks
- Collaborate with product research and platform teams
- Design and execute fine tuning experiments for large language models
- Design evaluation suites for model quality and capability
- Document training methodology results and decisions
- Implement parameter efficient fine tuning techniques
- Implement safety refusal and policy evaluations
- Lead dataset construction and quality assurance
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate GPU cluster training jobs and recover training state
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production ready fine tuning recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Direct Preference Optimization | Efficient Attention | FSDP | GPU clusters | Human Feedback | Hyperparameter Tuning | Learning from Human Feedback | LoRA | Machine Learning | Mixed Precision | Model Reproducibility | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Sequence Packing | Supervised Learning | Transformers | Zero
Education
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