LLM Engineer
Tasks
- Build dataset construction curation and quality assurance processes
- Build scalable training pipelines with distributed frameworks
- Design evaluation suites with automated benchmarks and human evaluation
- Design fine tuning experiments for large language models
- Document training methodology results and decisions
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning best practices and evaluation rigor
- Operate large scale training jobs on GPU clusters
- Optimize training throughput with mixed precision sequence packing and efficient attention
- Translate LLM research into production ready fine tuning workflows
- Tune hyperparameters and optimizer configurations
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter methods | Attention Mechanisms | Benchmarking | DPO | DeepSpeed ZeRO | Direct Preference Optimization | Evaluation methodology | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Learning from Human Feedback | LoRA | Mixed Precision | Mixed-precision training | Pipeline parallelism | Preference optimization | PyTorch | Python | QLoRA | RLHF | Reinforcement Learning | Reinforcement Learning from Human Feedback | Sequence Packing | Supervised Learning | Transformer Models
Education
Roles
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