Member Technical Staff - Applied AI Engineer (US Timing)

• The frontier: support is one of the richest applied AI environments — an agent has to gather context, reason across systems, use tools, take bounded action, verify the outcome, and communicate clearly. You design that system.

• The trust bar: AI cannot be trusted by default with customer support. You define state, permissions, guardrails, evals, and escalation paths so the automation earns trust.

• The feedback loop: it's immediate. Deploy into real support work, watch where the system fails, and improve it until people can hand over more of the job.

ROLES AND RESPONSIBILITIES

What you'll build

• 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.

How you'll work

• 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.

WHAT YOU'LL BRING

• 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.

Workflow and product judgment
• You can model a support function as states, tools, permissions, handoffs, verification steps, and escalation rules.

• You know what should be automated, what should remain human, and how to increase autonomy without reducing trust.

• You fix the class of problem, not only the visible symptom.

OPTIONAL

• 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.