LLM Engineer
Tasks
- Build scalable distributed training pipelines
- Collaborate with product research and platform teams
- Design and execute fine tuning experiments for large language models
- Design evaluation suites for benchmarks and human evaluation
- Document training methodology results and decisions
- Implement parameter efficient fine tuning techniques
- Implement safety refusal and policy evaluations
- Lead dataset construction and quality assurance for instruction tuning and preference data
- Manage model artifacts and ensure reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate large scale training jobs on GPU clusters
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production ready fine tuning recipes
- Tune hyperparameters and training stability strategies
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter-Tuning | Direct Preference Optimization | Efficient Attention | Evaluation methodology | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Learning from Human Feedback | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Fine Tuning | Transformers | Zero
Education
Roles
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