Member of Technical Staff, Machine Learning

Member of Technical Staff, Machine LearningLocation: SF Bay Area or Tokyo, Japan Type: Full-time About Radical Numerics Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced the first hybrid architectures that unlocked million-token context windows, enabling the first AI-designed whole genomes and real gene-editing tools. About the Role As a Member of Technical Staff at Radical Numerics, you will play a pivotal role in designing and building the computational foundations necessary to scale biological world models. You will work across the stack, integrating distributed training systems, architecture design, and scaling laws, to translate cutting-edge research into state-of-the-art models. What You'll Do Design large-scale training recipes for multimodal biological world models, including data curricula and scaling protocols (Training track). Implement distributed infrastructure, custom kernels, and systems instrumentation to maximize throughput (Infrastructure & Systems track). Elevate engineering and research standards across Radical Numerics through documentation, blogs, technical reports, and papers. What We're Looking For Proven track record in world-class engineering and/or fundamental research in large-scale training infrastructure, frontier model pre/post-training, or high-throughput data pipelines. Proficiency in building production-quality software (e.g., Python, PyTorch, CUDA, C++) with a focus on performance and reliability. Excellent written and verbal communication skills bridging technical and scientific domains. Intellectual curiosity with a bias toward experimentation, iteration, and continuous improvement. Nice to Have Contributions to open-source ML systems or tooling. Familiarity with modern MLOps, experiment tracking, and evaluation frameworks. Background in applied math, systems, computational biology, or related quantitative sciences. Why Radical Numerics Help produce the multimodal biological world models that will power rapid detection, response, and countermeasures across global health. Collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and national research institutes. Competitive compensation, comprehensive benefits, and support for continual learning. How to Apply Send your resume, a brief note on why Radical Numerics resonates with you, and examples of relevant public codebases you've built. We review applications on a rolling basis.

Created: 2026-03-10