Backend Engineer, Enterprise AI

Backend Engineer, Enterprise AI

Turing accelerates superintelligence to drive real economic progress. The company works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and research benchmarks that improve model capabilities across software engineering, enterprise knowledge work, and advanced STEM reasoning. On the enterprise side, Turing builds and deploys end-to-end agentic AI systems within mission-critical workflows for Fortune 500 companies across financial services, life sciences, healthcare, retail, automotive, and consumer packaged goods. By operating at both the research and deployment levels, Turing connects frontier research directly to enterprise needs.

The Enterprise AI team is building the platform that enables enterprises to deploy and operate AI systems in production. You will join as a Backend Engineer reporting to the Head of Engineering, Enterprise AI, working in a hybrid model with regular in-office presence in the New York City area, specifically near Madison Square Park.

What you'll do

• Design, build, and operate backend services and APIs supporting agent orchestration, model routing, enterprise integrations, permissions, evaluation systems, and usage metering.

• Develop production systems using tool use, agent loops, retrieval, prompt versioning, and agent versioning.

• Build and maintain integrations with document stores, ticketing systems, data platforms, and other enterprise systems.

• Design data models, identity systems, permissions, and service boundaries that enable secure multi-tenant operation.

• Own features end-to-end from architecture through implementation, testing, deployment, observability, and ongoing production operation.

• Create reusable connectors, services, and agent capabilities that scale across multiple customer deployments.

What you'll bring

• At least six years of backend or full-stack engineering experience on production SaaS or enterprise platforms.

• Deep expertise with Python or TypeScript services, API design, asynchronous job systems, and production operations.

• Hands-on experience with enterprise identity protocols including OAuth, OIDC, SAML, or SCIM, plus multi-tenant data modeling and access control.

• Production experience building LLM features involving tool use, agent loops, retrieval, prompt versioning, or agent versioning.

• Track record shipping LLM features to paying customers and owning production systems after launch.

• Experience integrating with document stores, ticketing systems, data platforms, or similar enterprise systems.

Nice to have

• Experience working on agent framework internals.

• Experience implementing SOC 2 or similar compliance controls.

• Experience building integrations for financial services or other regulated industries.

• Production experience building on OpenAI or Anthropic APIs.

What they offer

• Work at the frontier of AI innovation with the speed, ownership, and impact of a startup.

• Collaborate with exceptional colleagues who have deep AI experience from Google, Meta, Amazon, and other leading technology companies.