LLM Engineer
Tasks
- Build distributed training pipelines
- Construct and curate training datasets
- Design fine tuning experiments for large language models
- Document training results and decisions
- Implement evaluation suites for models
- Implement parameter efficient fine tuning
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate large scale GPU training jobs
- Optimize training throughput with mixed precision
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters for stable training
Perks/Benefits
Skills/Tech-stack
Adapter Layers | Attention Optimization | Benchmarks | DPO | Data Generation | Dataset Distillation | Dataset curation | Distributed Training | Efficient Fine Tuning | Evaluation | FSDP | Fine Tuning | GPU clusters | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Parameter efficient fine-tuning | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Red Teaming | Reinforcement Learning | Responsible AI | Sequence Packing | Supervised Learning | Synthetic Data Generation | Synthetic data | Transformers | Zero
Education
Roles
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