LLM Engineer
Tasks
- Build dataset construction and quality assurance for instruction tuning and preference data
- Build scalable training pipelines with distributed training frameworks
- Design and execute fine tuning experiments for large language models
- Design and run evaluation suites with automated benchmarks and human evaluation
- Document training methodology and results
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning evaluation and responsible deployment
- Operate large scale training jobs on GPU clusters with failure recovery
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production ready fine tuning workflows
- Tune hyperparameters and optimizer configurations for training stability
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter-Tuning | DPO | Dataset curation | DeepSpeed ZeRO | Distributed Training | Efficient Attention | Evaluation methodology | FSDP | Fine Tuning | GPU Cluster | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | LoRA | Mixed Precision | Optimization | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Reproducibility | Sequence Packing | Supervised Learning | Transformer Models
Education
Roles
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