LLM Fine-Tuning Engineer
Tasks
- Build scalable training pipelines
- Collaborate with cross-functional teams
- Design evaluation suites
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement parameter efficient fine tuning
- Implement safety refusal and policy evaluations
- Lead dataset construction and quality assurance
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning best practices
- Operate training jobs on GPU clusters
- Optimize training throughput
- Stay current with LLM research and translate to production
- Tune hyperparameters and training stability
Perks/Benefits
Skills/Tech-stack
Adapter methods | Automated benchmarking | Cluster operations | Dataset curation | Direct Preference Optimization | Distributed Training | Efficient Attention | FSDP | GPU Cluster | GPU Cluster Operations | Human Feedback | Human evaluation | Hyperparameter Tuning | Learning from Human Feedback | LoRA | Mixed Precision | Model Safety Evaluation | Model safety | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | Q LoRA | Red Teaming | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Transformer Models | Zero
Education
Roles
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