LLM Fine-Tuning Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build scalable training pipelines
- Collaborate with cross functional teams on fine tuning roadmaps
- Construct curate and QA datasets
- Design fine tuning experiments for large language models
- Document training methods results and decisions
- Implement evaluation suites and benchmarks
- Manage model artifacts and ensure reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate training jobs on GPU clusters
- Optimize training throughput with mixed precision
- Run safety refusal and policy evaluations
- Stay current with LLM research and deploy production ready recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Attention Optimization | DPO | Direct Preference Optimization | Distributed Training | Evaluation | FSDP | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Learning from Human Feedback | LoRA | Machine Learning | Mixed Precision | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Red Teaming | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Transformer Models | Zero
Education
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