LLM Engineer
Tasks
- Apply parameter-efficient fine-tuning methods
- Build dataset construction and quality assurance pipelines
- Collaborate with cross functional teams on fine tuning roadmaps
- Create automated benchmarks and human evaluation plans
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement distributed training pipelines
- Implement safety refusal and policy evaluations
- Manage model artifacts and reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate GPU cluster training jobs
- Optimize training throughput with mixed precision
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter based Fine Tuning | Attention Optimization | Cluster management | DPO | Distributed Training | Evaluation methodology | FSDP | Fine Tuning | GPU Cluster | GPU Cluster Management | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Model Safety Evaluation | Model safety | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Red Teaming | Reproducibility | Safety evaluation | Sequence Packing | Supervised Learning | Transformer Models | Zero
Education
Roles
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