LLM Fine-Tuning Engineer
Tasks
- Build instruction tuning and preference datasets
- Build scalable distributed training pipelines
- Design automated and human evaluation suites
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement parameter efficient fine tuning with LoRA or QLoRA
- Implement safety and policy evaluation workflows
- Manage model artifacts lineage 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
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters and training stability strategies
Perks/Benefits
Skills/Tech-stack
Adapter Layers | Automated Benchmarks | Cluster operations | DeepSpeed ZeRO | Direct Preference Optimization | Efficient Attention | Evaluation Design | FSDP | Fine Tuning | GPU Cluster | GPU Cluster Operations | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Mixed-precision training | Model Safety Evaluation | Model safety | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Fine Tuning
Education
Roles
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