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, Inference at Radical Numerics, you will build and optimize the systems that bring frontier biological AI models into production. Your work will focus on delivering state-of-the-art inference performance for large-scale genome and multimodal biological models across a wide range of real-world applications, including therapeutics, diagnostics, synthetic biology, and biodefense.

This is a highly technical role at the intersection of AI systems, distributed computing, and model deployment. You will work closely with research, infrastructure, and external partners to ensure our models can be efficiently deployed, scaled, and integrated into production environments. Success in this role requires deep expertise in large language model inference, kernel optimization, GPU systems, and performance engineering.

You should be excited by questions such as: How do we reduce inference latency for 100B MoE models? How do we maximize throughput across heterogeneous hardware environments? How do we optimize custom kernels for emerging hybrid model architectures? How do we deploy foundation models reliably across cloud, on-premise, and highly regulated environments? How do we enable our partners to transform biological research and development through production-grade AI systems?

What You'll Do

Drive end-to-end performance improvements. Identify and eliminate bottlenecks across the inference stack, from model execution and memory management to networking, scheduling, and hardware utilization.

Develop high-performance inference primitives. Build and optimize GPU kernels, numerical operators, and serving infrastructure to maximize throughput, latency, and efficiency on modern accelerator platforms.

Partner with external customers and collaborators. Work directly with pharmaceutical companies, biotech organizations, research institutions, and government partners to deploy models in production environments and solve challenging technical problems.

Build scalable deployment infrastructure. Create systems for serving, monitoring, benchmarking, and operating foundation models reliably across cloud, enterprise, and secure environments.

Collaborate with research and platform teams. Ensure new model architectures can be efficiently deployed at scale and help translate frontier AI research into real-world impact.

What We're Looking For

Expertise in large-scale AI inference systems. Proven experience optimizing, deploying, and operating LLMs or other foundation models in production environments.

Strong performance engineering and kernel development skills. Deep understanding of GPU architectures and experience with CUDA, Triton, or equivalent technologies for building high-performance numerical software.