About the Role
The role involves designing, implementing, and analyzing novel pre-training approaches to enhance the capabilities and efficiency of large-scale language models. This includes experimentation with training data, architectures, and optimization techniques.
Responsibilities
- Design and execute experiments to improve model pre-training
- Analyze training dynamics and identify performance bottlenecks
- Develop methods to increase data efficiency during training
- Collaborate on scaling strategies for larger models and datasets
- Evaluate the impact of architectural choices on model outcomes
- Implement and test new optimization algorithms
- Conduct ablation studies to validate methodological changes
- Contribute to the development of training infrastructure
- Monitor and interpret model behavior across training phases
- Iterate on training pipelines to improve stability and throughput
- Investigate the effects of data composition and filtering
- Explore techniques for reducing computational costs
- Publish findings internally and potentially in external venues
- Work closely with engineering teams to integrate research
- Maintain reproducibility and rigor in experimental design
- Assess alignment-relevant behaviors emerging during pre-training
- Support the creation of evaluation frameworks for pre-trained models
- Refine data curation pipelines for quality and diversity
- Explore novel training objectives and loss functions
- Contribute to documentation and knowledge sharing within the team
- Identify risks associated with large-scale training runs
- Optimize hyperparameter selection processes
- Evaluate generalization across domains and tasks
- Collaborate on interdisciplinary approaches to model development
- Stay current with advancements in machine learning and NLP
Nice to Have
- PhD in machine learning, artificial intelligence, or related discipline
- Publications at top-tier machine learning conferences
- Hands-on experience with language model pre-training
- Contributions to open-source machine learning projects
- Experience with model parallelism and tensor partitioning
- Background in computational linguistics
- Familiarity with reinforcement learning concepts
- Knowledge of causal inference methods
- Experience in high-performance computing environments
- Prior work on data-efficient training methods
Compensation
Competitive salary and benefits package
Work Arrangement
Full-time, on-site or hybrid options available
Team
Part of the core research team focused on foundational model development
Research Culture
- We emphasize curiosity-driven investigation balanced with practical impact
- Collaboration between researchers and engineers is strongly encouraged
- Time is allocated for deep work and independent exploration
- Regular internal seminars and paper discussions are held
- Transparency in research decisions and findings is prioritized
Safety Focus
- Research is conducted with attention to potential misuse and risks
- Proactive evaluation of emergent model behaviors is standard practice
- Safety considerations are integrated into model design choices
- Ongoing assessment of training data impacts is performed
- Cross-team collaboration ensures safety is a shared priority
Available for qualified candidates
