LLM Fine-Tuning Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build scalable distributed training pipelines
- Conduct safety refusal and policy evaluations
- Construct curate and QA training datasets
- Design fine tuning experiments for large language models
- Implement evaluation suites and benchmarks
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning best practices
- Operate training jobs on GPU clusters
- Optimize training throughput with mixed precision
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Attention Optimization | Cluster computing | DPO | FSDP | Fine Tuning | GPU Cluster | GPU cluster computing | Human evaluation | Language Models | Large Language Models | LoRA | Mixed Precision | Model Evaluation | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Reproducibility | Safety evaluation | Sequence Packing | Supervised Fine Tuning | Zero
Education
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