Role overview
There are two primary tracks within the team:
1️⃣ Model Architecture & Pre-Training Research
- Design and build large-scale models from first principles
- Experiment with architecture design and scaling behavior
- Explore attention variants, normalization strategies, routing mechanisms, and training stability
- Run rigorous ablation experiments and performance analysis
- Work on training infrastructure and model scaling
2️⃣ Post-Training & LLM Optimization
- Fine-tune and align large language models for complex applications
- Develop advanced post-training techniques and evaluation methods
- Integrate LLM systems into large-scale decision platforms
- Optimize models for performance, efficiency, and reliability
What you'll work on
- Designing and training advanced machine learning models
- Developing research pipelines in Python / PyTorch
- Running controlled experiments and analyzing model performance
- Applying statistical and probabilistic reasoning to large datasets
- Collaborating with researchers and engineers to translate ideas into production systems
- Solving challenging problems involving large-scale compute, model optimization, and real-world data
What we're looking for
Candidates from environments such as:
- Frontier AI labs
- Advanced research groups
- Quantitative research firms
- High-performance ML infrastructure teams
- Deep learning research organizations
Tags & focus areas
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