LLM Engineer
Tasks
- Build instruction tuning and preference datasets
- Collaborate on fine tuning roadmap with product and research
- Construct scalable distributed training pipelines
- Create evaluation suites with automated and human evaluation
- Design fine tuning experiments for large language models
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning evaluation and deployment best practices
- Operate GPU based training jobs and recover training state
- Optimize training throughput with mixed precision and efficient attention
- Tune hyperparameters and training stability strategies
Perks/Benefits
Skills/Tech-stack
Adapter Layers | Attention Optimization | Benchmarking | DPO | Evaluation methodology | FSDP | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Safety evaluation | Sequence Packing | Supervised Learning | Transformers | Zero
Education
Roles
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