LLM Engineer
Tasks
- Build evaluation suites with automated and human evaluation
- Build scalable training pipelines
- Construct curate and QA training datasets
- Design fine tuning experiments for large language models
- Implement parameter efficient fine tuning
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning evaluation and deployment
- Operate large scale training jobs on GPU clusters
- Optimize training throughput with mixed precision and efficient attention
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter methods | Automated Benchmarks | Cluster management | DPO | Efficient Attention | Evaluation methodology | FSDP | GPU Cluster | GPU Cluster Management | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Sequence Packing | Supervised Learning | Transformers | Zero
Education
Roles
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