LLM Fine-Tuning Engineer
Tasks
- Build scalable distributed training pipelines
- Collaborate with product and research teams to align roadmaps
- Construct curate and validate instruction and preference datasets
- Create automated and human evaluation suites
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement parameter efficient fine tuning with LoRA QLoRA and adapters
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and responsible deployment
- Operate GPU training jobs and recover from failures
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters optimizer and training stability
Perks/Benefits
- N/A
Skills/Tech-stack
Adapters | DPO | Data Generation | Dataset Distillation | Distributed Training | Efficient Attention | Evaluation | FSDP | GPU Cluster | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Safety evaluation | Sequence Packing | Supervised Learning | Synthetic Data Generation | Synthetic data | Transformer | Zero
Education
Roles
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