LLM Fine-Tuning Engineer
Tasks
- Build dataset construction and quality assurance processes
- Build scalable training pipelines
- Design evaluation suites including 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 lineage tracking and reproducibility
- Mentor engineers on fine tuning best practices
- Operate training jobs on GPU clusters and recover failures
- Optimize training throughput with mixed precision and efficient attention
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter methods | DPO | Dataset curation | Distributed Training | Efficient Attention | Evaluation benchmarks | FSDP | GPU clusters | Human evaluation | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | PyTorch | Python | QLoRA | RLHF | Safety evaluation | Sequence Packing | Transformers | Zero
Education
Roles
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