LLM Engineer
Tasks
- Build scalable training pipelines for large models
- Collaborate with product research and engineering stakeholders
- Design and execute fine tuning experiments for large language models
- Design evaluation suites for automated and human evaluation
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Lead dataset construction and quality assurance
- Manage model artifacts and ensure reproducibility
- Mentor engineers on fine tuning and evaluation practices
- Operate large scale training jobs on GPU clusters
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production fine tuning workflows
- Tune hyperparameters and training stability for fine tuning
Perks/Benefits
Skills/Tech-stack
Adapter-Tuning | Benchmarking | DPO | Distributed Training | Efficient Attention | Evaluation | FSDP | Fine Tuning | GPU Cluster | Human evaluation | Hyperparameter Tuning | LoRA | Mixed Precision | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Safety evaluation | Sequence Packing | Supervised Learning | Transformer | Zero
Education
Roles
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