Member of Technical Staff, Pretraining Science
Hybrid (SF Bay Area or Tokyo, Japan)full-timePosted 7mo ago
About the role
Role focused on the science of how biological world models learn during large-scale pretraining, blending research and engineering to develop new pretraining methods, study scaling, and design training recipes for efficiency, generalization, and downstream scientific usefulness.
Responsibilities
- Research and develop new pretraining methodologies. Explore how biological world models learn from multi-modal data (eg, sequence, structure, and image data), and develop new objectives, training strategies, or architectural ideas that improve representation quality and downstream performance.
- Study scaling behavior. Investigate how training dynamics change with model size, data composition, context length, and compute budget. Use empirical results to inform scaling protocols and future research priorities.
- Design data curricula and sampling strategies. Build and refine mixtures, curricula, and sampling policies that improve learning efficiency, generalization, and robustness across biological modalities and tasks.
- Work on architecture, algorithms, and optimization. Evaluate ideas in model design, optimization, long-context learning, and training stability that make large-scale biological pretraining more effective.
- Run large-scale experiments rigorously. Design, execute, and analyze experiments with strong empirical discipline. Distinguish real effects from bugs, noise, or benchmark artifacts, and convert findings into better training recipes.
- Collaborate closely with infrastructure and data teams. Work across the stack to ensure large-scale experiments are reproducible, efficient, and instrumented well enough to support fast scientific iteration.
- Define evaluations for pretraining progress. Build and improve evaluation suites that measure representation quality, long-context behavior, transfer to downstream biological tasks, and scientific utility.
Qualifications
- Strong track record in ML research or engineering, especially in large-scale model training, pretraining, representation learning, optimization, scaling laws, or related areas.
- Ability to design, run, and analyze experiments thoughtfully, with strong research judgment and empirical rigor.
- Proficiency in Python and modern deep learning tooling such as PyTorch, plus comfort debugging distributed or high-performance training systems at scale.
- Experience working in distributed or high-performance computing environments.
- Excellent written and verbal communication skills, especially the ability to explain complex technical findings clearly across engineering, research, and scientific collaborators.
- Intellectual curiosity and a bias toward experimentation, iteration, and continuous improvement.
Skills
Required:Pretraining methodology designScaling and experimental rigor
Benefits
- Competitive compensation
- Comprehensive benefits
- Support for continual learning
Last updated May 28, 2026. Interested? Apply on the company site.
About Radical Numerics
Next-gen AI lab for recursive self-improvement
Radical Numerics - Next-gen AI lab for recursive self-improvement
AIAI labrecursive self-improvement
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