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About the Role

As a Member of Technical Staff, Inference, you will build and optimize 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, requiring deep expertise in large language model inference, kernel optimization, GPU systems, and performance engineering.

Responsibilities

Drive end-to-end performance improvements by identifying and eliminating bottlenecks across the inference stack.
Develop high-performance inference primitives, including GPU kernels, numerical operators, and serving infrastructure.
Partner with external customers and collaborators to deploy models in production environments and solve challenging technical problems.
Build scalable deployment infrastructure for serving, monitoring, benchmarking, and operating foundation models.
Collaborate with research and platform teams to ensure efficient deployment of new model architectures and translate AI research into real-world impact.

Requirements

Expertise in large-scale AI inference systems with proven experience optimizing, deploying, and operating LLMs or other foundation models in production.
Strong performance engineering and kernel development skills, with a deep understanding of GPU architectures and experience with CUDA, Triton, or equivalent technologies.
Systems-level thinking with the ability to diagnose and solve bottlenecks across the full stack.
Strong software engineering fundamentals with proficiency in Python and modern ML frameworks such as PyTorch.
Customer and deployment orientation with experience delivering production AI systems that solve real-world problems.
Excellent technical communication skills for effective collaboration across diverse teams.

Skills

Large-scale AI inference
LLM optimization
Foundation model deployment
Performance engineering
Kernel development
GPU architectures
CUDA
Triton
Python
PyTorch
Systems-level thinking
Distributed computing
Model deployment
vLLM
TensorRT-LLM
SGLang
DeepSpeed
Open-source AI infrastructure
Compilers
Kernel libraries
Distributed systems
Cloud infrastructure
Large-scale GPU clusters
Biological foundation models
Computational biology
Genomics
Scientific AI applications
Regulated AI systems

Location

Remote

Work Type

Full-time

Experience Level

Member of Technical Staff

About the Company

Radical Numerics is an AI research lab building general biological intelligence with the mission to master the code of life and reduce human suffering.
The team created Evo, starting the field of generative genomics, with work featured in Science and presented at 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 applies distributed systems, model architecture, and numerics research to biology, redesigning the foundation model training stack to turn scientific data into intelligible, generative models.
The company was founded to develop both the power to design and the responsibility to defend against AI-generated bioweapons, recognizing the inseparable nature of these forces.

Equal Opportunity

Radical Numerics is committed to equal employment opportunity and does not discriminate in any employment opportunities or practices based on an individual's race, color, creed, gender (including gender identity and gender expression), religion (all aspects of religious beliefs, observance or practice, including religious dress or grooming practices), marital status, registered domestic partner status, age, national origin or ancestry (including language use restrictions and possession of a driver’s license issued under California Vehicle Code section 12801.9), natural hair, physical or mental disability, political affiliation, medical condition (including cancer or a record or history of cancer, and genetic characteristics), sex (including pregnancy, childbirth, breastfeeding or related medical condition), genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws.
It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics.