LLM Fine-Tuning Engineer
Tasks
- Build dataset construction curation and quality assurance
- Build scalable distributed training pipelines
- Collaborate with product research and platform teams
- Design evaluation suites with automated benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology results and decisions
- Implement parameter efficient fine tuning using LoRA QLoRA and adapters
- Implement safety refusal and policy evaluations
- 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 with mixed precision sequence packing and efficient attention
- Stay current with LLM research and translate advances into recipes
- Tune hyperparameters optimizer configurations and training stability
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter methods | Automated Benchmarks | DPO | Dataset curation | Deep learning | Efficient Attention | Efficient Fine Tuning | Evaluation | FSDP | Fine Tuning | GPU clusters | Human evaluation | Hyperparameter Tuning | Instruction Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Model safety | Optimizer Configuration | Parameter efficient fine-tuning | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Reproducibility | Sequence Packing | Supervised Fine Tuning | Supervised Learning | Zero
Education
Roles
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