AI Engineer (Agent Platform)

• We’re building the agent platform that powers NewsBreak’s next-generation AI products — from local-news synthesis agents that millions of Americans wake up to, to paid-growth agents that autonomously decide what to advertise and where. One platform, several agentic products, real users, real spend, real consequences

• We’re hiring our first dedicated Agent Platform engineer to own this layer end-to-end. You’ll join a small, focused team that ships weekly, works closely with product, and takes eval and observability seriously. Our codebase already runs multiple agents in production — and you’ll help build what comes next

• Build the agent runtime that orchestrates context assembly, tool invocation, model routing, and workflow tracking across multiple agentic products — the foundational layer that other teams build on top of

• Design the eval and observability harness that runs thousands of agent traces per day, surfaces regressions before they ship, and turns production failures into actionable improvements

• Own the context engineering layer — retrieval, ranking, compression, and memory — that determines what makes it into a model call; contribute informed opinions on RAG vs. long-context vs. structured tool returns

• Integrate and benchmark new foundation models as they become available; make principled decisions about model selection across agents based on capability and cost

• Build user-facing surfaces — playgrounds, agent traces, control panels — and ship them to production; the internal product team relies on these tools daily

• Collaborate closely with product to take new agent concepts from early brief to working v1 in weeks

Benefits

• Work from home opportunities

• Paid time off and paid holidays

• Paid parental leave

• FSA and commuter benefit programs

• Team activity budget

• Top-tier 401(K) plan with company matching

• Health, dental, and vision care for you and your family- Product sensibility: willingness to push back when something feels off, and a habit of thinking about the end user alongside the technical architecture

• Active, hands-on familiarity with modern AI development tooling (Cursor, Claude Code, Codex, v0, or equivalents) and a clear sense of how and when to apply them

• Demonstrable experience shipping an AI product with real users — a side project, internal tool, open-source agent, or startup MVP you can speak to concretely

• Strong proficiency in Python (or Go / Node) with solid backend engineering experience — APIs, databases, queues, caches — and an understanding of how system design needs evolve with scale

• Sufficient frontend capability (React or Next.js) to ship functional internal tools independently

• Developed perspective on AI agent design: context management, tool-calling protocols, eval strategy, and the tradeoffs between fine-tuning, prompting, and scaffolding

• Ability to work end-to-end independently: backend, frontend, deployment, instrumentation, and iteration — without needing a fully defined spec to get started

• Experience integrating LLMs with complex, real-world data (news, ads, geo, user behavior) at scale

• Benefits

• Hands-on experience with eval frameworks (LM-eval, custom harnesses, LLM-as-judge), prompt iteration workflows, or fine-tuning (LoRA / RLHF / DPO)

• A public artifact — GitHub repo, technical blog, paper, or demo — that reflects how you approach problems

• Experience building or operating a multi-agent system in production (orchestration, sub-agents, MCP, skills)