LLM Engineer
Tasks
- Align fine tuning roadmaps with business needs
- Build dataset construction and QA processes
- Create scalable distributed training pipelines
- Design fine tuning experiments for large language models
- Develop evaluation suites with automated and human evaluation
- Document training methodology and results
- Implement parameter efficient fine tuning techniques
- Implement safety refusal and policy evaluations
- Manage model artifacts and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate large scale training jobs on GPU clusters
- 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
- N/A
Skills/Tech-stack
Adapters | DeepSpeed ZeRO | Direct Preference Optimization | Efficient Attention | FSDP | GPU clusters | Human Feedback | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Sequence Packing | Supervised Learning | Transformers
Education
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