LLM Engineer
Tasks
- Build distributed training pipelines
- Collaborate on fine tuning roadmaps
- Construct and curate instruction datasets
- Create automated benchmark evaluations
- Design fine tuning experiments
- Document training methods and results
- Implement RLHF and DPO training
- Implement parameter efficient fine tuning
- Implement safety and policy evaluations
- Manage model artifacts and reproducibility
- Mentor engineers on best practices
- Operate GPU training jobs and recovery
- Optimize training throughput
- Run human evaluation and probes
- Tune hyperparameters and optimizers
Perks/Benefits
Skills/Tech-stack
Adapters | Direct Preference Optimization | Distributed Training | Efficient Attention | Efficient Fine Tuning | Evaluation | FSDP | Fine Tuning | GPU clusters | Human Feedback | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Parameter efficient fine-tuning | Pipeline parallelism | Preference optimization | PyTorch | Python | Q LoRA | Reinforcement Learning | Reinforcement Learning from Human Feedback | Sequence Packing | Supervised Learning | Transformer | Zero
Education
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