Applied AI Engineer - WorkOS

About WorkOS

WorkOS builds modern developer tools and APIs that make it easy for companies to become Enterprise Ready. Our platform powers authentication, identity, authorization, and other critical infrastructure that developers need to securely scale their products to large organizations.

WorkOS powers enterprise features for many of the fastest-growing AI companies, including OpenAI, Cursor, Perplexity, Sierra, and Plaid. As AI reshapes software, WorkOS is at the frontier of Human and Agent Authentication, Identity, and Access Control helping companies answer a new critical question: who are your agents, and what are they allowed to do?

About the Role

We're growing our Applied AI team to dramatically increase productivity across Engineering, Sales, Support, and Operations, and to ship AI-powered products that customers rely on directly.

As an Applied AI Engineer, you'll design and ship production AI systems that change how WorkOS builds, sells, supports, and scales. You’ll also be building things that WorkOS customers use, and systems that the entire company depends on daily. This is a 0->1 role with company-wide visibility.

What you'll do

• Design and ship customer-facing AI products like ask.workos.com, AI support bots embedded in customer Slack channels, and new surfaces
• Build internal tools that become part of people's daily work: agents, automations, and workflows that are stable, observable, and easy to maintain
• Work on big bets: a unified bot framework, a sandboxed coding harness agent, and infrastructure that lets the entire company ship
• Use LLMs, embeddings, retrieval, and tool-calling to plug into docs, Slack, GitHub, CRM, analytics, support systems, and internal services
• Replace repetitive, multi-step manual processes with orchestrated, AI-driven flows that span multiple apps and data sources
• Stay current on new models and tooling, run focused experiments, and help the team converge on patterns, libraries, and infrastructure that compound over time

What we're looking for

• You've taken AI-powered systems from idea to production and through at least one iteration cycle with real users
• Strong engineering fundamentals and comfort owning services, data flows, and integrations end-to-end
• Experience building with LLM APIs
• Focus on failure modes, observability, and ownership
• Bias toward action and removing real bottlenecks
• Comfort with ambiguity and fast change

Nice to have

• Prior work on customer-facing AI products
• Experience with embeddings, retrieval/RAG architectures, and structured tool-calling or agents
• Exposure to MCP or similar protocols for connecting AI agents to real systems