Platform Engineer - Enterprise AI

Skills this role screens for

About the role

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

What you get

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, Kubernetes, Helm, Terraform or Pulumi, and GitOps.

Reliability and observability ownership: Experience carrying a pager, running incidents, and working with tools such as OpenTelemetry, Prometheus, or Datadog.

Data and platform systems experience: Familiarity with Postgres, object storage, queues, and vector stores.

Candidates whose experience is limited to cloud operations or ticket-queue DevOps without software engineering, or who have never operated multi-tenant systems, will not be a fit for this role.

Also

Top-Tier Candidates Will Have

• Experience with model serving and model gateways, including vLLM, LiteLLM, or Triton.
• Experience implementing SOC 2 controls in CI.
• Experience deploying and operating AI systems in regulated or highly controlled environments.

Also

Values

• We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value.
• We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection
• We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.

Also

Advantages of joining Turing

Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

For applicants from the European Union, please review Turing's GDPR notice here.

• Work at the frontier of AI, helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks.
• Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS.
• Bring frontier AI innovation to the enterprise, applying lessons learned from leading AI labs to solve real-world business challenges.
• Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies.
• Move at the pace of AI innovation, with the speed, ownership, and impact of a startup.