LLM Engineer
Tasks
- Build scalable training pipelines for distributed training
- Construct and curate instruction tuning and preference datasets
- Design fine tuning experiments for large language models
- Develop evaluation suites with automated benchmarks and human evaluation
- Implement parameter efficient fine tuning techniques
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning evaluation and responsible deployment
- Operate 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 optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapters | DPO | Dataset curation | Deep reinforcement learning | Efficient Attention | Efficient Fine Tuning | Evaluation benchmarks | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Parameter efficient fine-tuning | Pipeline parallelism | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Transformers | Zero
Education
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