LLM Engineer
Tasks
- Build dataset construction and quality assurance processes
- Build scalable training pipelines with distributed training
- Design evaluation suites with benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology and results
- Implement RLHF and DPO fine tuning workflows
- Implement parameter efficient fine tuning with LoRA
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage and reproducibility
- Mentor engineers on fine tuning and evaluation
- Operate training jobs on GPU clusters and recover from failures
- Optimize training throughput with mixed precision and attention
- Translate LLM research into production fine tuning recipes
- Tune hyperparameters and optimizer configurations
Perks/Benefits
Skills/Tech-stack
Adapter-Tuning | Automated benchmarking | DPO | Dataset curation | Direct Preference Optimization | Distributed Training | Efficient Attention | Evaluation | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Machine Learning | Mixed Precision | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Red Teaming | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Transformer Models | Zero
Education
Roles
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