Staff AI Engineer, Agentic Automation

Cerby; United States; Remote; USD 210,000 - 320,000; Full time

Job summary

Cerby is seeking a Staff AI Engineer to own and scale AI agents that onboard applications and repair automations. You will lead production-grade systems handling real customer credentials with a strong emphasis on security and reliability.

You will drive end-to-end ownership from architecture and roadmap to on-call incident response, self-healing workflows, and cost-aware evaluation across browser automation, API integration, and LLM tooling.

Qualifications

• Shipped an LLM agent with tool use in production and owned a backend system.
• Experience in end-to-end AI automation, from design to on-call.
• Strong in Python or TypeScript with hands-on browser automation or API integration.

Responsibilities

• Own agentic onboarding and self-healing end to end: architecture, roadmap, and on-call once live.
• Take existing systems to production on durable workflows with observability.
• Detect drift, diagnose breaks, repair workflows, and re-validate fixes.
• Decide what heals automatically and what requires human review.
• Own benchmarks for specs, workflows, and repairs; manage cost and latency.
• Own threat model: prompt injection, credential isolation, and secure browsing.
• Collaborate with integrations, automation-runtime, and platform teams; mentor contributors.

Skills

Python

TypeScript

LLM agents

Tool integration

Eval benchmarks

Browser automation

Distributed systems

Security

Tools

Playwright

Temporal

AWS

OpenAPI tooling

LLM tooling

Job description

OVERVIEW & IMPACT

Every application Cerby protects needs Cerby to know how it works — its API, or the clicks behind its login, password rotation, MFA setup, or access change — and that knowledge breaks whenever a vendor redesigns its site or changes its API. We are looking for a Staff AI Engineer to own the AI agents that onboard new applications and repair automations when they drift. You will start with two agent systems we have already built and benchmarked:

API catalog generation: reads a vendor’s public API docs in a headless browser and produces a validated OpenAPI spec, with per-job cost ceilings and an accuracy benchmark.

Browser workflow generation: performs a task in a SaaS app’s web UI and writes a replayable automation workflow, handling MFA, one-time codes, and CAPTCHAs along the way.

Your job is to take them to production, make them self-healing, and set the direction for agentic automation at Cerby. These agents handle real customer credentials and browse untrusted sites, so security is part of the job, not an afterthought.

KEY RESPONSIBILITIES

Ownership and Production

• Own agentic onboarding and self-healing end to end: architecture, roadmap, and on-call once live.
• Take the existing systems to production on durable workflows (Temporal), with the observability to run them.

Self-Healing Automation

• Detect drift — a changed login page, a moved button, a renamed API field — from failed runs, before customers notice.
• Build agents that diagnose the break, repair the workflow or spec, and re-validate the fix against real runs before it goes back into service.
• Decide what heals automatically and what needs human review.

Evaluation and Cost

• Own the benchmarks for generated specs, workflows, and repairs, and make “good enough to ship” a measured threshold.
• Own cost and latency per output, choosing between frontier and open-weight models on measured accuracy versus cost.

Security

• Own the threat model: prompt injection from page content, credential isolation, token handling, sandboxed browsing, and egress.
• Keep credentials out of prompts, logs, queues, and stored run state.

Collaboration

• Partner with the integrations, automation-runtime, and platform teams; write the design docs they build on, and mentor contributors.

QUALIFICATIONS & REQUIRED SKILLS

Experience

• You have shipped an LLM agent with tool use that ran unattended for real users, and owned a backend system in production.

Technical Skills

• Strong in Python or TypeScript , and productive in both.
• LLM agents and tool use : agent loops, structured outputs, retries, and the failure modes they create.
• Eval discipline : you have built benchmarks or eval harnesses and used them to make decisions.
• Depth in browser automation (Playwright or similar) or backend and API-integration systems , and comfort in the other.
• Distributed systems : queues, idempotency, and durable workflow engines (Temporal, Step Functions, or similar).
• Security judgment for systems that handle secrets, including agent-specific threats such as prompt injection.

Desirable

• Temporal in production; Pydantic AI or a comparable agent framework.
• Open-weight model serving (Fireworks, Together, Bedrock) and LLM cost modeling.
• Computer-use agents or browser extensions (Chrome MV3); OpenAPI tooling.
• Identity, IAM, or credential-management experience; AWS and Terraform.

Soft Skills

• Clear technical writing and trade-offs other teams can depend on.
• You take an early system to production without waiting to be told how, and care that it keeps working after launch.

WHAT SUCCESS LOOKS LIKE

• 6 months: both systems run in production for real workloads, with improving error rates and cost per output, and drift is detected automatically.
• 12 months: onboarding a new application is predominantly automated, and most drift-driven breakages heal without a human.