Foundation Model Engineer
Tasks
- Build scalable training pipelines for transformer models
- Design evaluation suites for automated and human evaluation
- Design fine tuning experiments for large language models
- Implement parameter-efficient fine-tuning methods
- Implement safety refusal and policy evaluations
- Lead dataset construction curation and quality assurance
- Manage model artifacts lineage tracking and reproducibility
- Mentor engineers on fine tuning and evaluation best practices
- Operate large scale training jobs on GPU clusters
- Optimize training throughput with mixed precision and efficient attention
- Translate LLM research into production ready fine tuning recipes
- Tune hyperparameters optimizer configurations and training stability
Perks/Benefits
- N/A
Skills/Tech-stack
Adapter methods | Attention Mechanisms | Dataset curation | Direct Preference Optimization | Distributed Training | Efficient Fine Tuning | Evaluation & benchmarking | FSDP | Fine Tuning | GPU clusters | Human Feedback | Human evaluation | Hyperparameter Tuning | Language Models | Large Language Models | Learning from Human Feedback | LoRA | Mixed Precision | Model Artifacts | Model Reproducibility | Parameter efficient fine-tuning | Pipeline parallelism | Policy Evaluation | Preference optimization | PyTorch | Python | QLoRA | RLHF | Refusal Detection | Reinforcement Learning | Reinforcement Learning from Human Feedback | Safety evaluation | Sequence Packing | Supervised Learning | Training Pipeline | Transformer Models | Zero
Education
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