~2h ago

Staff AI Engineer, Agentic Automation

(united States)Remotelead

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

architecture, roadmap, and on-call once live. Take the existing systems to production on durable workflows (Temporal), with the observability to run them.

Requirements Summary

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.

Technical Tools

Machine Learning EngineerData

Responsibilities

~1 min read

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.

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.

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.

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.

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

Requirements

~1 min read

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

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.

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.

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.

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.