Member of Technical Staff, ML Infrastructure

About Cognita
Cognita’s mission is to increase the world’s access to healthcare. Radiology is (1) the first-line diagnostic specialty, (2) facing a worsening global workforce shortage, and (3) highly digitized, making it uniquely positioned for AI to have an enormous impact. Stage one of Cognita is focused on expanding access to radiology at scale.

Our founding team met at Stanford, where they laid the groundwork for applying comprehensive AI to radiology. Building on that foundation, Cognita develops vision-language models that read radiology studies the way radiologists do - interpreting the full study in clinical context - and generate draft results that make radiologists more efficient and accurate. In partnership with Radiology Partners, Cognita’s models are trained and validated on one of the world’s largest real-world radiology datasets.

About the Role

As a Member of Technical Staff on the ML Infrastructure team, you will build and operate the platform that enables Cognita’s ML systems to scale reliably in training and production. Your focus will be on distributed systems, model serving, and the infrastructure that supports large-scale training and deployment of vision-language models used in clinical workflows.

Your Impact

• Build and operate distributed infrastructure for ML training and model serving at scale.

• Own systems that manage large-scale compute, storage, and data movement for ML workloads.

• Design and optimize model serving systems and APIs for extreme reliability, latency, and throughput.

• Support large distributed training runs by providing robust, scalable infrastructure primitives.

• Ensure infrastructure can handle large radiology studies, including multiple series and prior exams.

• Improve observability and reliability for ML systems in production.

• Work closely with ML training engineers to turn training requirements into scalable systems.

• Debug performance and reliability issues across the ML stack.

What You Bring

• We are not credential-driven. We only look for evidence of exceptional ability. Please show us what you have built previously.

• Strong experience in ML infrastructure, MLOps, or distributed systems.

• Experience building or operating infrastructure for large-scale ML systems.

• Deep understanding of systems performance, reliability, and scalability.

• Ownership mindset and bias toward building robust long-lived platforms.

What Sets You Apart

• Experience with industry-standard distributed computing frameworks.

• Experience deploying large language or vision-language models.

• Experience with inference optimization techniques

• Experience with cloud infrastructure (e.g., AWS / GCP / Azure).

• Experience with GPU-accelerated infrastructure.

• Experience operating ML systems in production environments.

Our Culture

• We’re an in-person team based in the San Francisco Bay Area. Working together every day helps us move faster, learn from each other, and build strong relationships. We believe the best work happens when great people are in the same room.

• We build from first principles. We question assumptions, reason from fundamentals, and execute with speed and clarity, without sacrificing quality.

• We operate as a true meritocracy. Impact matters, rather than optics. Feedback is direct, respect is non-negotiable, and we support each other with the ownership, trust, and resources needed to do the best work of our careers.

• Every decision we make is grounded in supporting clinicians to improve patient outcomes.