Foundation Model Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build scalable distributed training pipelines
- Collaborate with teams to align fine tuning roadmaps
- Construct curate and QA instruction tuning and preference datasets
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement evaluation suites with automated and human evaluation
- Implement safety and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate training jobs on GPU clusters and recover training state
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter Layers | DPO | Dataset curation | DeepSpeed ZeRO | Distributed Training | Efficient Attention | Evaluation methodology | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Model lineage | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Reproducibility | Safety evaluation | Sequence Packing | Supervised Learning | Transformer
Education
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