Researcher, Training
Researcher developing and scaling architectures and optimization techniques for flagship large language models. The role requires experience contributing to major LLM training runs, strong knowledge of inference and Transformer efficiency, and an empirical approach to experiments and debugging.
About the job
Responsibilities
- Design, prototype, and scale new architectures to improve model intelligence.
- Execute and analyze experiments autonomously and collaboratively.
- Study, debug, and optimize model performance and computational performance.
- Contribute to training and inference infrastructure.
Requirements
- Experience landing contributions to major LLM training runs.
- Ability to thoroughly evaluate and improve deep learning architectures independently.
- Motivation to safely deploy LLMs in the real world.
- Strong understanding of state-of-the-art Transformer modifications for efficiency.
- Deep understanding of LLM architectures and model inference.
- Hands-on empirical approach to research and development.
Compensation
- Annual salary: 170000–445000.
- Hybrid schedule with three days per week in the office.
- Relocation assistance for new employees.
- Option to work from home on Thursdays and Fridays.
- Office amenities include adjustable desks, phone booths, conference rooms, stocked kitchens, and spaces to unwind or collaborate.
Skills
LLMs, Deep Learning, Transformer Architectures, Model Inference, Model Training, Efficient Attention, Long-Context Modeling, Optimization, Scaling, Experiment Design, Model Evaluation, Python, Training Infrastructure, Inference Infrastructure
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