LLM Fine-Tuning Engineer
Tasks
- Apply parameter efficient fine tuning techniques
- Build dataset construction curation and quality assurance
- Design evaluation suites with automated and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology and results
- Execute supervised fine tuning and preference optimization
- Implement safety refusal and policy evaluations
- Implement scalable distributed training pipelines
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate GPU cluster training jobs and recover from failures
- Optimize training throughput with mixed precision and efficient attention
- Tune hyperparameters optimizer and training stability
Perks/Benefits
- N/A
Skills/Tech-stack
Adapters | Attention | DPO | Dataset curation | Distributed Training | Evaluation | FSDP | GPU clusters | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | Q LoRA | RLHF | Red Teaming | Safety evaluation | Sequence Packing | Supervised Learning | Transformers | Zero
Education
Roles
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