Software Engineer, WorkOS

• 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
• 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

Desirable

• We’re building an Applied AI function 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
• Adapts quickly as models, tools, and best practices evolve
• This is a 0→1 role with company‑wide visibility
• 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
• Example problems you might work on:
• Turning noisy, cross‑tool workflows (tickets, Slack threads, docs) into a single agent that handles triage, routing, and suggested actions
• Building AI‑assisted engineering workflows that shorten the loop from “bug report or idea in Slack” to changes safely deployed in production
• Designing a GTM agent that helps reps research accounts, prepare meetings, and follow up using context from CRM, product usage, and prior conversations