LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines using distributed training frameworks
- Collaborate with cross functional teams on fine tuning roadmaps
- Construct curate and quality assure training datasets
- Design and run 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 methods
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate large scale GPU training jobs and recover from failures
- Optimize training throughput with mixed precision and efficient attention
- Stay current with LLM research and apply advances to production recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter-Tuning | Attention Optimization | Benchmarking | DPO | Dataset Distillation | Dataset curation | Direct Preference Optimization | Evaluation methodology | FSDP | GPU Computing | Human Feedback | Human evaluation | Learning from Human Feedback | LoRA | Machine Learning | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Sequence Packing | Supervised Learning | Synthetic data | Transformer | Zero
Education
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