LLM Engineer
Tasks
- Build distributed training pipelines
- Construct and curate instruction and preference datasets
- Create evaluation suites with benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methods and results
- Implement parameter-efficient fine-tuning methods
- Implement safety and policy evaluation
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning evaluation and deployment
- Operate GPU cluster training jobs and recover failures
- Optimize training throughput with mixed precision
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter | DPO | Dataset Distillation | Deep learning | Efficient Attention | Evaluation | FSDP | Fine Tuning | GPU Cluster | Human evaluation | LoRA | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Sequence Packing | Supervised Learning | Synthetic data | Transformer | Zero
Education
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