LLM Fine-Tuning Engineer
Tasks
- Build dataset construction and quality assurance for instruction tuning and preference data
- Build scalable training pipelines with distributed training frameworks
- Collaborate with product research and platform teams on fine tuning roadmaps
- 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 methods
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning evaluation and responsible deployment
- Operate large scale training jobs on GPU clusters and recover from failures
- Optimize training throughput with mixed precision sequence packing and efficient attention
- Translate LLM research advances into production ready fine tuning recipes
- Tune hyperparameters and optimizer configurations for training stability
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter-Tuning | Attention Optimization | Benchmarking | Cluster operations | DPO | Dataset curation | Evaluation methodology | FSDP | GPU Cluster | GPU Cluster Operations | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Model Reproducibility | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Refusal Prediction | Reinforcement Learning | Safety evaluation | Sequence Packing | Supervised Learning | Transformers | Zero
Education
Roles
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