Senior Engineering Manager

Senior Engineering Manager

Location: SF Bay Area or Tokyo, Japan
Type: Full-time

About Radical Numerics

Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the language of life, and our purpose is to reduce human suffering.

Our team created Evo, and started the field of generative genomics. Our work was featured on the cover of Science, and presented by our CEO on the main stage of TED2025. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome from scratch. Evo 2, featured in Nature, is the largest fully open source AI project across any domain.

Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We’ve redesigned the foundation model training stack to turn the world’s raw scientific data (e.g. biological sequences, experiments, and physical processes), into intelligible, generative models that can expand and accelerate what humanity can understand, design, and cure.

We are building a new type of AI research lab, driving both the frontier of biological AI for human health, and building the systems to defend against its potential misuse. The same generative breakthroughs that enable life saving cures also lowers the barrier to creating engineered threats and AI-generated bioweapons. We believe these forces are inseparable. Radical Numerics was founded to develop both the power to design and the responsibility to defend.

About the Role

We’re hiring a Senior Engineering Manager to lead a team working across ML infrastructure, training systems, and research engineering in support of biological world models.

This is a hybrid leadership role for someone who can grow strong engineers, raise the quality bar, and help teams execute on technically ambitious work. You will partner closely with technical leads, researchers, and company leadership to set direction for critical systems and translate that direction into reliable, high-velocity execution.

This role combines management with technical depth. You should be comfortable leading senior engineers, helping shape system architectures and stepping into complex technical discussions when needed. The ideal candidate has experience in high-performance systems, distributed training, data infrastructure, or other environments where research velocity depends on strong engineering foundations.

What You’ll Do

• Build and lead a strong engineering team. Hire selectively, mentor deeply, and maintain a high bar for execution and code quality.
• Own core infrastructure. Drive development of systems for large-scale model training and experimentation—training/inference, data pipelines, evals, and internal tools.
• Set technical direction. Make clear architectural tradeoffs (performance vs. speed vs. flexibility), and guide where to invest vs. keep things simple.
• Accelerate research. Improve reproducibility, observability, and debugging to enable faster iteration and more reliable experiments.
• Stay close to the work. Partner tightly with researchers and step into design, debugging, and ambiguous problems when needed.

What We’re Looking For

• Track record leading engineering teams in technically demanding environments, ideally where infrastructure and research are closely linked.
• Experience managing senior engineers and enabling high-autonomy teams.
• Strong background in distributed systems, data systems, developer platforms, or similarly complex technical areas.
• Ability to work credibly with researchers and senior engineers on system architectures, prioritization, and execution.
• Strong technical judgment, especially around tradeoffs, sequencing, and operating under ambiguity.
• Comfortable staying close to the work, including going deep on design or debugging when needed.
• Excellent communication skills and the ability to align stakeholders across engineering, research, and science.
• High standards for rigor, speed, and practical decision-making.

Nice to Have

• Familiarity with large-scale pretraining and model serving workflows, multimodal data, and research platforms.
• Experience in companies or teams where engineering supported frontier research or fast-moving technical R&D.
• Track record of hiring and developing engineering leaders in addition to individual contributors.

Why Radical Numerics

• Help build the multimodal biological world models needed for rapid detection, response, and countermeasures across global health. 
• Work on foundational engineering problems at the intersection of AI systems, large-scale model training, and biology. 
• Join a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and research institutes. 
• Competitive compensation, comprehensive benefits, and support for continual learning.