LLM Engineer
Tasks
- Build scalable distributed training pipelines
- Construct curate and quality assure instruction tuning and preference datasets
- Design evaluation suites for automated benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Implement parameter efficient fine tuning using LoRA QLoRA and adapters
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate GPU cluster training jobs and recover failed runs
- Optimize training throughput with mixed precision and sequence packing
- Translate LLM research advances into production ready training workflows
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter | Automated benchmarking | DPO | Distributed Training | Evaluation | FSDP | GPU Cluster | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Pipeline parallelism | PyTorch | Python | QLoRA | RLHF | Safety evaluation | Sequence Packing | Transformers | Zero
Education
Roles
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