LLM Engineer
Tasks
- Build scalable training pipelines
- Collaborate with product and research teams on fine tuning roadmaps
- Construct curate and QA training datasets
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement evaluation suites benchmarks and human evaluation
- Implement parameter efficient fine tuning
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning best practices
- Operate training jobs on GPU clusters
- Optimize training throughput with mixed precision and efficient attention
- Stay current with LLM research and productionize fine tuning recipes
- Tune hyperparameters and training stability
Perks/Benefits
Skills/Tech-stack
Adapter-Tuning | DPO | Distributed Training | Efficient Attention | Evaluation | FSDP | Fine Tuning | GPU Cluster | Human evaluation | LoRA | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Safety evaluation | Sequence Packing | Supervised Learning | Transformers | Zero
Education
Roles
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