Senior Engineering Manager

Radical Numerics•San Francisco, CA

4d•Hybrid

About The Position

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.

Requirements

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 Haves

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.

Responsibilities

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.