> Markdown version of [/jobs/ext/4519506-senior-infrastructure-engineer-applied-ai](https://www.wearedevelopers.com/jobs/ext/4519506-senior-infrastructure-engineer-applied-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Infrastructure Engineer, Applied AI - **Company:** Wintermeyer Ventures - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $250,000.0 - $350,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Big Data, Distributed Systems, Elasticsearch, Python (Programming Language), Node.Js, Redis, Evaluation Pipelines, Build Management, Kubernetes, Information Technology, Apache Kafka, Human in the Loop, Data Generation - **Published:** November 2, 2026 - **Apply:** https://www.juju.com/job/16_391ade1d ## About the Role * 6 or more years of experience building and maintaining platform infrastructure, with the ability to contribute across the stack. * Experience designing and operating production distributed systems or platform infrastructure, including systems that manage data at scale. * Strong Kubernetes expertise and experience with AWS or GCP. * Proficiency with Node.js or Python; experience with Kafka, Redis, and Elasticsearch is valuable. * Computer science education or equivalent strong technical foundations. * Experience at a startup or in a data-intensive technology environment is helpful. * Authorized to work in the United States without relying on student visa status. ## Description Join the infrastructure team at an applied AI company building data and evaluation systems for AI development. You will create the shared platform that supports data generation workflows, human-in-the-loop systems, and evaluation pipelines. What You'll Do * Design and build core infrastructure for data generation, human-in-the-loop workflows, and evaluation pipelines. * Own foundational systems and improve their scalability, reliability, and performance. * Partner with engineering teams to provide platform services that help them build effectively. * Architect distributed systems for large datasets and high-throughput workloads. * Establish engineering practices for system design, deployment, and reliability. * Mentor junior engineers and help foster a collaborative engineering culture.