About the role
About NewsBreak
Local life runs on local information. What happened down the street? Who can fix this? Where should we go tonight? NewsBreak started in 2015 to answer these questions, and today more than 40 million people a month rely on us to follow what’s going on in their zip code.
Three platforms with one vision: powering local life. Led by founder and CEO Jeff Zheng, NewsBreak is our local life and news platform; Nearby AI connects people with local businesses; and NDot AI helps publishers and creators grow and earn.
Building for the long term. A $115 million Series C made us a unicorn in 2021, and we keep investing in our people. We’re Great Place to Work® certified and named to Fast Company’s Best Workplaces for Innovators (2025) and Fortune’s Best Workplaces in the Bay Area™ (2026).
Everyone makes an impact. Whether you write code, shape products, tell our story, or run the business, we want people who own projects end to end, stay curious, and speak up. Join us.
Learn more at: NewsBreak, Nearby AI, NDot AI
About the Role
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.
Responsibilities
- 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
Requirements
- 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
- 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
- Ability to work end-to-end independently: backend, frontend, deployment, instrumentation, and iteration — without needing a fully defined spec to get started
- Developed perspective on AI agent design: context management, tool-calling protocols, eval strategy, and the tradeoffs between fine-tuning, prompting, and scaffolding
- 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
- Product sensibility: willingness to push back when something feels off, and a habit of thinking about the end user alongside the technical architecture
Preferred Qualifications
- Experience building or operating a multi-agent system in production (orchestration, sub-agents, MCP, skills)
- 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 integrating LLMs with complex, real-world data (news, ads, geo, user behavior) at scale
Benefits
We offer a competitive benefits package:
- Health, dental, and vision care for you and your family (100% coverage for employee)
- Top-tier 401(K) plan with company matching
- Paid time off and paid holidays
- FSA, HSA and commuter benefits programs
- Team activity budget
The US base salary range for this full-time position is listed below. Pay may vary based on a number of factors including job-related skills, level, experience, geographic location and relevant education or training. At NewsBreak, we design our overall rewards package to attract top talents. Depending on the position, the role may also be eligible for discretionary bonus and options. Your recruiter can share more details during the hiring process.
Annual Base Pay Range
$120,000—$220,000 USD
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