LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines using distributed training frameworks
- Collaborate with cross functional teams to align fine tuning roadmaps
- Design and execute fine tuning experiments for large language models
- Design evaluation suites including automated benchmarks and human evaluation
- Document training methodology results and decisions
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Lead dataset construction curation and quality assurance
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning best practices and responsible deployment
- Operate large scale training jobs on GPU clusters
- Optimize training throughput using mixed precision and efficient attention
- Stay current with LLM research and translate advances into production
- Tune hyperparameters and optimizer configurations for training stability
Perks/Benefits
Skills/Tech-stack
Adapters | Attention Optimization | Cluster operations | Data Generation | DeepSpeed ZeRO | Direct Preference Optimization | Distributed Training | Evaluation methodology | FSDP | GPU Cluster | GPU Cluster Operations | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Model Reproducibility | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Synthetic Data Generation | Synthetic data
Education
Roles
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