LLM Engineer
Tasks
- Build training pipelines for large language models
- Conduct automated benchmarks human evaluation and probes
- Construct and curate instruction and preference datasets
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement LoRA QLoRA and adapter fine tuning
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning and evaluation rigor
- Operate GPU cluster training jobs and recover training state
- Optimize training throughput with mixed precision sequence packing and efficient attention
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter-Tuning | Attention Optimization | Cluster operations | Deep learning | Direct Preference Optimization | FSDP | GPU Cluster | GPU Cluster Operations | Human Feedback | Learning from Human Feedback | LoRA | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Sequence Packing | Supervised Learning | Transformers | Zero
Education
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