LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines
- Collaborate with product research and engineering teams
- Construct curate and QA training datasets
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement evaluation suites with automated and human evaluation
- Implement parameter efficient fine tuning
- Manage model artifacts and ensure reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate large scale GPU training jobs
- Optimize training throughput with mixed precision
- Run safety refusal and policy evaluations
- Translate LLM research advances into production recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter methods | DPO | Dataset curation | Distributed Training | Efficient Attention | Efficient Fine Tuning | Evaluation benchmarks | FSDP | Fine Tuning | GPU Cluster | Human evaluation | Human-in-the-loop | Hyperparameter Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Parameter efficient fine-tuning | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Red Teaming | Reinforcement Learning | Safety evaluation | Sequence Packing | Supervised Fine Tuning | The Loop | Transformer | Zero
Education
Roles
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