LLM Fine-Tuning Engineer
Tasks
- Build scalable distributed training pipelines
- Construct curate and quality assure training datasets
- Design and execute evaluation benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement parameter efficient fine tuning
- Implement safety refusal and policy evaluations
- Manage model artifacts and ensure reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate large scale GPU training jobs and recover training state
- Optimize training throughput with mixed precision and efficient attention
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapters | DPO | Dataset curation | Efficient Attention | Efficient Fine Tuning | Evaluation benchmarks | FSDP | Fine Tuning | GPU clusters | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Parameter efficient fine-tuning | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Reproducibility | Safety evaluation | Sequence Packing | Supervised Fine Tuning | Transformer Models | Zero
Education
Roles
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