Platform Engineer - Enterprise AI

About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Role: Enterprise AI - Platform Engineer Location: New York City, Madison Square Park area Reports to: Head of Engineering Experience Level: 6+ years Work Model: Hybrid, with regular in-office presence Why Join Enterprise AI? Enterprise AI is building the platform that enables reliable deployment of AI systems across enterprise environments. As a Platform Engineer, you will build and operate the core of Turing’s Enterprise AI platform, including platform services, the model gateway, multi-tenant infrastructure, CI/CD, infrastructure as code, and observability. The central challenge is creating repeatable deployments across environments Turing may not control, including client cloud accounts, dedicated instances, and sovereign installations. As a Platform Engineer, You Will Build Core Platform Services Design, build, and operate the services that support the Enterprise AI platform, including platform infrastructure and the model gateway. Enable Enterprise Deployment Create reliable deployment systems for client cloud accounts, dedicated instances, and sovereign environments. Own Infrastructure and Delivery Systems Build and maintain CI/CD, infrastructure as code, and deployment tooling across environments using Kubernetes, Helm, Terraform or Pulumi, and GitOps practices. Build for Reliability Design systems that support secure multi-tenant operation, reliable service ownership, and clear operational accountability. Establish Observability Build observability across the platform using service-level objectives, structured logging, metrics, tracing, and alerting. Build FinOps Visibility Develop telemetry that tracks model and compute costs by tenant. The Anti-Sell Hands-on software engineering: This is not a cloud-operations or ticket-queue DevOps role. You must be able to write production software and contribute deeply to platform services. Operational ownership: You will participate in on-call operations and incident response and help build the processes required to operate a production platform. Deployment complexity: The platform must operate across client cloud accounts, dedicated instances, and sovereign environments. Team accountability: The platform must be observable, operable, and maintainable by the team rather than dependent on one individual. Our Enterprise AI Platform Engineers Typically Have Production platform engineering experience: Six or more years of platform or infrastructure engineering experience supporting production SaaS. Customer-controlled deployment experience: Experience shipping a product that customers run in their own environment, including in a VPC, a self-hosted environment, or an air-gapped environment. Strong software engineering depth: Deep expertise in at least one backend language, such as Go, Ruby, Python, or TypeScript, with the ability to read and work across the others. Cloud and infrastructure expertise: Deep experience in at least one cloud and comfort working in a second, including private networking, KMS, IAM, Kub