Member of Technical Staff, Inference

The role

You will build and optimize systems to bring frontier biological AI models into production. The role focuses on delivering inference performance for large-scale genome and multimodal biological models.

What you will do

• Drive end-to-end performance improvements across the inference stack.
• Develop high-performance inference primitives including GPU kernels and serving infrastructure.
• Partner with external customers and collaborators to deploy models in production.
• Build scalable deployment infrastructure for serving, monitoring, and benchmarking foundation models.
• Collaborate with research and platform teams to deploy new model architectures at scale.

Required qualifications

• Experience optimizing, deploying, and operating LLMs or other foundation models in production environments.
• Deep understanding of GPU architectures.
• Experience with CUDA, Triton, or equivalent technologies for building high-performance numerical software.
• Ability to diagnose and solve bottlenecks across the full stack.
• Proficiency in Python and modern ML frameworks such as PyTorch.
• Experience working closely with users, customers, or cross-functional stakeholders.
• Ability to collaborate effectively across research, engineering, infrastructure, and scientific teams.

Nice to have

• Experience with inference frameworks such as vLLM, TensorRT-LLM, SGLang, DeepSpeed, or similar systems.
• Contributions to open-source AI infrastructure, inference frameworks, compilers, or kernel libraries.
• Experience with distributed systems, cloud infrastructure, and large-scale GPU clusters.
• Familiarity with biological foundation models, computational biology, genomics, or scientific AI applications.
• Experience operating AI systems in regulated, secure, or mission-critical environments

About the company

Radical Numerics is an AI research lab building general biological intelligence. Their mission is to master the code of life to reduce human suffering.