Software Engineer - Applied AI

WorkOS•San Francisco, CA

17h•Remote

About The Position

WorkOS is building an Applied AI team to dramatically increase productivity across Engineering, Sales, Support, and Operations. As an Applied AI Engineer, you’ll design and ship production AI systems that change how WorkOS builds, sells, and supports software. Your focus is internal: building durable tools and workflows that teammates rely on every day. You’ll work on a small, high‑ownership team that chooses problems based on measurable impact, moves from idea → prototype → production in days or weeks, and adapts quickly as models, tools, and best practices evolve. This is a 0→1 role with company‑wide visibility.

Requirements

You’ve taken AI‑powered systems from idea to production and through at least one iteration cycle with real users.
Strong engineering fundamentals. You’re comfortable owning services, data flows, and integrations end‑to‑end.
This role must pass our engineering interview loop.
Experience with LLM APIs, embeddings, retrieval / RAG‑style architectures, and structured tool‑calling (or agents), even if not all in production yet.
You’re comfortable stitching together APIs, webhooks, event systems, and internal tools, and you think about failure modes, observability, and ownership.
Bias toward action. You care less about showing off a model and more about removing real bottlenecks, saving hours, or unlocking workflows the company couldn’t do before.
Comfort with ambiguity and fast change. You enjoy working in areas where the problem, the tools, and the “right” patterns are evolving quickly – and you’re excited to help define them.

Nice To Haves

Prior work with enterprise SaaS, identity / auth, or security‑sensitive systems.
Exposure to MCP or similar protocols for connecting AI agents to real systems.

Responsibilities

Work closely with Engineering, Sales, Support, and Ops to uncover high‑leverage internal problems where AI can remove bottlenecks or unlock new workflows.
Design and build tools that become part of people’s daily work – agents, automations, and workflows that are stable, observable, and easy to maintain.
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 infra that compound over time.