Applied AI Engineer

Responsibilities

• Design and ship customer-facing AI products like 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
• Example problems you might work on
• 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

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