Member Technical Staff - Applied AI Engineer (US Timing)

At Composio, we are building infrastructure that allows AI agents to communicate with the tools people use for work, including GitHub, Gmail, Notion, Salesforce, and more. We are a small team of engineers working across context, auth, execution, and reliability to build the action layer for AI agents.

We raised a $25M Series A from Lightspeed, with angel investors including Guillermo Rauch (CEO of Vercel), Dharmesh Shah (CTO of HubSpot), and Gokul Rajaram. Our customers range from YC startups to companies like Brex, Glean, Zoom, and more.

• Agentic workflows that take over support functions: intake, classification, context gathering, reproduction, diagnosis, response drafting, remediation, verification, and follow-up.

• Tool-using agents that safely inspect runs, logs, auth state, configuration, and provider behavior, then propose or execute bounded recovery actions.

• Eval suites for diagnostic correctness, resolution quality, safe escalation, customer communication, and end-to-end task completion.

• Human-in-the-loop systems that make ownership, uncertainty, approvals, handoffs, and failure states explicit.

• The observability, memory, and feedback loops that let these agents improve from real support cases without repeating mistakes.

• Product improvements that remove entire classes of customer issues instead of handling the same symptoms faster.

• Start with the customer outcome, then work backward into the model, workflow, tooling, or product change required to deliver it.

• Own the hardest issues end to end when direct engineering is needed: reproduce, isolate, mitigate, ship, communicate, and verify the resolution with the customer.

• Sit with customers and partner with Support, FDE, Product, and Engineering to find work that should become software.

• Move from a rough prototype to a reliable production workflow, with traces, evals, guardrails, and a clear human fallback.

• Use frontier models, coding agents, and internal AI tools every day to multiply your own engineering output.

• Measure success in customer outcomes improved, support work removed, and classes of failure prevented.

• Experience level: 2–4 years of professional software engineering experience.

Applied AI engineering

• You have built production systems with language models, tool calling, retrieval, structured outputs, multi-step workflows, or agent memory.

• You know how to use models and coding agents as leverage, and where their non-determinism creates hidden risk.

• You use evals, traces, failure analysis, and fast iteration to make AI systems dependable.

Strong systems engineering

• You are an experienced backend, platform, or integration engineer who has shipped and operated production software.

• You can debug across SDKs, HTTP, OAuth, webhooks, queues, data stores, logs, and third-party APIs.

• You turn an ambiguous failure into a minimal reproduction, a root cause, and a durable fix.

Customer-adjacent building
• You are deliberately choosing to work closer to customers because you want a tighter loop between what you build and the impact it creates.

• You enjoy talking directly with technical users, turning a vague problem into a system, and watching that system change their outcome.

• You communicate clearly under uncertainty, ask the exact next question, and never bluff.

• TypeScript or Python in production.

• Experience building AI agents, copilots, workflow automation, or eval infrastructure.

• Experience with a developer platform, API product, support engineering, SRE, or incident response.

• Familiarity with OAuth 2.0, webhooks, rate limits, Postgres, queues, and cloud observability.

• Experience with support systems such as Plain, Zendesk, Intercom, or Salesforce.

• Public technical writing, open source contributions, or unusually good debugging notes.