LLM Engineer
Tasks
- Build distributed training pipelines
- Construct curate and QA datasets
- Design fine tuning experiments
- Develop evaluation suites and benchmarks
- Document training methodology and results
- Implement parameter efficient fine tuning
- Implement safety and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning best practices
- Operate GPU cluster training jobs
- Optimize training throughput with mixed precision
- Translate LLM research into production recipes
- Tune hyperparameters and training stability
Perks/Benefits
- N/A
Skills/Tech-stack
Adapters | Attention Optimization | DPO | Dataset curation | Evaluation methodology | FSDP | Fine Tuning | GPU clusters | Human evaluation | LoRA | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Red Teaming | Sequence Packing | Supervised Learning | Synthetic data | Transformers | Zero
Education
Roles
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