Software Engineer - Applied AI

WorkOS - United States & Canada

Engineer Verified May 21, 2026 Source: workos.com

This role is part of the Internal AI Job Board, a curated list of jobs for teams building or operating internal AI systems. Follow the original role page for the employer description, requirements, and application flow.

Responsibilities

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.
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
Ships at both layers: internal leverage and customer-facing product
Adapts quickly as models, tools, and best practices evolve
This is a 0→1 role with company-wide visibility.
Design and ship customer-facing AI products like ask.workos.com http://ask.workos.com, AI support bots embedded in customer Slack channels, and new surfaces we haven't built yet
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
A sandboxed coding harness that can safely take a bug report or feature spec all the way to a deployed change
A unified bot framework that powers every AI touchpoint, internal and customer-facing from a single, observable backbone
A GTM intelligence layer that gives reps live account context, meeting prep, and follow-up from CRM, product usage, and conversation history
Turning noisy, cross-tool workflows (tickets, Slack threads, docs) into a single agent that handles triage, routing, and suggested actions
Infrastructure that lets any WorkOS team ship a reliable internal AI app without reinventing the stack
You've taken AI-powered systems from idea to production and through at least one iteration cycle with real users