LLM Engineer
Tasks
- Build evaluation suites with automated benchmarks and human evaluation
- Build scalable training pipelines with distributed training frameworks
- Construct curate and QA instruction tuning and preference datasets
- Design fine tuning experiments for large language models
- Document training methodology results and decisions
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning best practices and evaluation rigor
- Operate large scale training jobs on GPU clusters
- Optimize training throughput with mixed precision sequence packing and efficient attention
- Stay current with LLM research and translate into production recipes
- Tune hyperparameters and optimizer configurations for training stability
Perks/Benefits
Skills/Tech-stack
DPO | Dataset curation | DeepSpeed | Evaluation | FSDP | Fine Tuning | GPU Computing | Human evaluation | Hyperparameter Tuning | LoRA | Machine Learning | Mixed Precision | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Sequence Packing | Supervised Learning | Transformers | Zero
Education
Roles
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