About Us

Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the code 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-generated 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.

In October, we introduced Omnii-1, the world's most advanced multimodal genome language model, and a leap toward general biological intelligence. It unifies 28 biological modalities, spanning DNA, RNA, proteins, epigenomics, and more, in a single model trained on trillions of tokens of biological data In a world first, Omnii-1 designed the first AI-generated bacteriophages to be administered to a human patient with an antibiotic-resistant infection under compassionate use. We released this news alongside seven other project areas, showing the model’s breadth of applications.

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

As a Member of Technical Staff, Distributed Systems at Radical Numerics, you will design, build, and operate the data center & cloud environment that powers our large-scale training and inference. You will deliver high-performance, reliable, and cost-efficient compute so our researchers can move fast at scale, turning frontier infrastructure into the foundation for the next generation of biological world models.

This role is ideal for someone who combines distributed systems, deep operational instincts and has an interest in modern machine learning. You should care about how every layer of the cluster affects research velocity: provisioning and capacity, scheduling and multi-tenancy, storage and lineage, communication overhead, observability, and the reliability of long-running jobs across thousands of accelerators.

What You'll Do

Operate and automate large GPU clusters. Own provisioning, imaging, and capacity planning across large distributed compute systems, with a focus on uptime, utilization, and cost efficiency.

Build a unified compute interface. Write software that abstracts cluster management and presents a single, ergonomic interface for training and inference, so researchers spend their time on science rather than infrastructure.

Extend scheduling and orchestration. Adapt systems like Kubernetes or Slurm for topology-aware placement, preemption, quotas, and fair-share multi-tenancy across competing workloads.

Maximize throughput and hardware efficiency. Profile and tune performance across the stack, including communication patterns, memory efficiency, custom kernels, compilation paths, and systems instrumentation, to ensure training compute is used effectively.

Improve reliability and recovery. Establish standards and mechanisms for robustness and error recovery, including monitoring, fault tolerance, checkpointing, and incident analysis for fast-moving research infrastructure.

Build reliable storage and artifact paths. Design durable paths for datasets, checkpoints, and logs, with clear retention and lineage that support reproducible, large-scale experimentation.

Collaborate across research and engineering. Partner closely with model researchers and training scientists to unblock large-scale runs, advise on parallelism and performance trade-offs, and design systems that support new scientific directions rather than constrain them.

What We're Looking For

Track record building distributed systems, or operating large-scale GPU clusters and container orchestration systems such as Kubernetes or Slurm.

Proficiency in building performant, maintainable software in at least one backend language (we use Python and Rust), with a focus on performance and reliability.