LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines
- Collaborate with product research and platform teams
- Conduct safety refusal and policy evaluations
- Construct curate and QA datasets for instruction tuning and preference data
- Design fine tuning experiments for language models
- Document training methodology and results
- Implement evaluation suites with benchmarks and human evaluation
- Implement parameter efficient fine tuning
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation
- 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
Skills/Tech-stack
Adapter-Tuning | Attention Mechanisms | Cluster computing | DPO | Deep learning | Distributed Training | Evaluation | FSDP | GPU Cluster | GPU cluster computing | Human Feedback | Hyperparameter Tuning | Learning from Human Feedback | LoRA | Mixed Precision | Pipeline parallelism | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Sequence Packing | Supervised Learning | Transformers | Zero
Education
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