LLM Engineer
Tasks
- Align fine tuning roadmaps with business needs
- Build scalable distributed training pipelines
- Construct and curate training datasets
- Design evaluation suites with benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement parameter efficient fine tuning
- Implement safety and policy evaluations
- Manage model artifacts and ensure reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate large scale GPU training jobs
- Optimize training throughput with mixed precision and sequence packing
- Stay current with LLM research and productionize training recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter-Tuning | Benchmarking | DPO | DeepSpeed ZeRO | Evaluation methodology | FSDP | GPU Computing | Human Feedback | Human evaluation | Learning from Human Feedback | LoRA | Mixed Precision | Mixed-precision training | Pipeline parallelism | Policy Evaluation | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Transformers
Education
Roles
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