Senior Software Engineer, Query
关于 CircleCI 工程的 CI/CD 已经解决了。现在它正在重新发明。人工智能代理人不仅运行管道。他们编写代码，触发构建，解释故障和提出修复。开发人员使用的界面来理解和相信工作还没有存在。我们在建造它们。在 Circle CI,我们的指导原则不是墙上的海报。我们快速学习：我们进行实验，接受失败，并扩展出效果。我们像客户一样认为：每一个决定都会连接到开发者坐在失败构建前2点钟。我们拥有结果：不仅是我们一片块，而是整个东西。如果你已经这样做了，你就在这里适合。关于查询团队 根据 CircleCI 的数据，我们可以在一个软件交付生命周期中获得最大优势.Flaky 测试检测，测试见解，管道搜索和为什么构建失败的代理都取决于快速可靠访问它。查询团队拥有使这一点成为可能的系统。我们从创建的交易服务中提取产品数据并存储其在用于历史和分析工作负载而建造的数据库中。我们通过域级 API 向 CircleCI 的产品团队以及客户提供这些信息。我们的目标是让数据存储为每一个团队都"火放忘了",并以不断增长的规模进行快速和可靠查询。你会做什么 · 建立人工智能原生地。人工智能是我们的工作方式。它不是工程上的一层生产力，而是问题如何解决和处理的默认起点。· 塑造我们的数据存储尺寸的方式。拥有和硬化我们的基于 ClickHouse 的存储层。设计已知和新兴访问模式的方案，调整摄入和查询性能来支持生产规模数据。· 构建移动我们的数据的管道。从事件流和基于定位的耐用工作流中设计和自主摄入。数据应该可靠地到达，在运输过程中保持可查询性，并且永远不会减慢产品和客户的关键路径。· 将数据服务于产品和用户。将我们的平台转化为历史查询的默认路径。使获取历史数据变得容易，而不是瓶。团队应能够快速获得他们需要的东西，并以最小的握手，同时查询可以充分了解如何使用数据来优化摄入量，并在每个消费者身上阅读。建立索引，c
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About CircleCI Engineering
CI/CD has been solved. Now it's being reinvented. AI agents don't just run pipelines. They write code, trigger builds, interpret failures, and propose fixes. The interfaces that developers use to understand and trust that work don't exist yet. We're building them.
At CircleCI, our guiding principles aren't posters on a wall. We learn fast: we run experiments, accept failure, and scale what works. We think like the customer: every decision connects back to the developer sitting in front of a failing build at 2 am. We own the outcome: not just our slice of it, but the whole thing. If that's how you already work, you'll fit here.
About the Query Team
Every pipeline, job, and test that runs on CircleCI generates data, and that history is one of our biggest advantages in an agentic software delivery lifecycle. Flaky test detection, test insights, pipeline search, and agents that reason about why a build failed all depend on fast, reliable access to it.
The Query team owns the system that makes this possible. We pull product data out of the transactional services that create it and store it in datastores built for historical and analytical workloads. We serve it through domain-level APIs to CircleCI's product teams and to customers through our public API. Our goal is to make storing data "fire and forget" for every other team, and querying it fast and dependable at a scale that keeps growing.
What You'll Do
- Build AI-natively. AI is how we work. It's not a productivity layer on top of engineering, but the default starting point for how problems get approached and solved.
- Shape how we store data at scale. Own and harden our ClickHouse-based storage layer. Design schemas for known and emerging access patterns, and tune ingestion and query performance to support production-scale data.
- Build the pipelines that move our data. Design and own ingestion from event streams and durable workflows built on Temporal. Data should arrive reliably, stay queryable while it's in transit, and never slow down the critical path of the services that produce it.
- Serve data to the product and to customers. Evolve our platform into the default path for historical queries. Make getting historical data easy, not a bottleneck. Teams should be able to get what they need quickly and with minimal hand-holding, while Query keeps enough visibility into how the data is used to optimize ingestion and reads across every consumer. Build in the indexing, caching, and tiered storage required to keep it performant.
- Set technical direction. When the problem is complex or the answer isn't obvious, you lead the design discussion, write up the architecture decision, and make the call. You help decide which data domain we migrate next and how.
- Champion observability. Use Honeycomb, Datadog, and Rollbar to understand ingestion lag, query latency, and data correctness. For a data platform, operational data drives stability, not just incident response.
- Keep things moving. You take ambiguous data problems from design to production quickly, and you help the people around you move faster too.
You don't just patch problems, you ask why they happened. You'll fix the immediate issue, then solve the underlying team practice, process, or system that let it through.
What You'll Bring
- AI-native engineering practices. You use AI tools daily and can speak to what you've built, where they changed your approach, and where they fall short.
- Hands-on data experience. You've built or owned data-intensive systems such as ingestion pipelines, event-driven architectures, or analytical and time-series datastores. You understand the tradeoffs between transactional and analytical storage, and you've designed schemas and tuned queries for high-volume workloads. Experience with ClickHouse or another columnar database is a strong plus.
- Backend depth in Go. You're strong in Go, or strong in a comparable language and eager to work in Go: interface design, data modeling, service integration, and concurrency. Familiarity with React and TypeScript is a plus, since you'll occasionally work on the product surfaces that display this data.
- Distributed systems judgment. You understand delivery guarantees, idempotency, backpressure, and failure recovery. You know when to rely on a framework's reliability guarantees rather than building your own.
- Technical leadership. You've led the design of complex systems and made tradeoffs deliberately. You leave foundations others can build on. You raise the quality bar through reviews, mentoring, and direct feedback, not just through the code you write.
- A track record of shipping. 5+ years building and operating production backend systems. Your work has served real users, and you've treated latency, reliability, and correctness as design concerns.
Sound like you?
You care about how data powers the product, not just where it lives. You know how to make a datastore fast at scale, and have tuned schemas and queries against real production load. You think like a platform engineer, measuring your work by how much it unblocks the teams building on it. AI isn't a tool you reach for occasionally; it's a core part of how you approach and solve problems. If that sounds like you, we want to talk.
Canada Base Pay Range
$156,500—$215,000 CAD
Expected salary range for San Francisco and the surrounding area.
US SF Bay Area (Metro Zone 3)
$185,000—$255,000 USD
Expected salary range for specific metro areas including Boston, Washington DC, Denver, Los Angeles, New York, Portland, San Diego, and Seattle. These metro areas may change periodically.
US Metro Zone 2
$175,000—$245,000 USD
Expected pay range for all other US locations not falling into Metro Zone 3 or Metro Zone 2.
US General
$165,000—$225,000 USD
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
About CircleCI
CircleCI is the world’s largest continuous integration/continuous delivery (CI/CD) platform, and the hub where code moves from idea to delivery. As one of the most-used DevOps tools - processing more than 3 million jobs a day - CircleCI has unique access to data on how the most effective engineering teams work, and the tools to help software companies successfully leverage the power of AI into their commercial applications. Companies like Hinge, HuggingFace, and Samsung use us to improve engineering team productivity, release better products, and get to market faster.
Founded in 2011 and headquartered in downtown San Francisco with a global, remote workforce, CircleCI is venture-backed by Base10, Greenspring Associates, Eleven Prime, IVP, Sapphire Ventures, Top Tier Capital Partners, Baseline Ventures, Threshold
Ventures, Scale Venture Partners, Owl Rock Capital, Next Equity Partners, Heavybit, and Harrison Metal Capital.
CircleCI is an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.
How CircleCI Uses AI in Hiring
At CircleCI, people make our hiring decisions. We use technology to support our recruiting team so they can spend more time on what matters most: getting to know you.
Where we may use AI
- Job descriptions and messaging: AI may help draft job descriptions and role-specific messaging so they are clear and accurate.
- Application review: For some roles, we use an AI tool to review applications, including resumes and application responses, against criteria our hiring team sets for the role. The tool generates a fit score and summary to help recruiters organize and prioritize applications.
- Interview notes: With advance notice, an AI note-taking tool may join interviews to record, transcribe, and summarize the conversation. This lets interviewers focus on talking with you. You will be told before each interview, and you may opt out at any stage by letting your recruiter know. If you opt out, the tool will not join, and your candidacy is not affected.
- Outreach: AI may help draft candidate outreach and scheduling messages, which our team reviews before sending.
Our commitment
AI does not make hiring decisions at CircleCI. Our recruiters and hiring teams review candidates and make every decision to advance or reject an application. No candidate is rejected based solely on an AI score. We have tools and processes in place to test for bias and to review how these tools perform, and we adjust our approach as AI technology and the laws that govern it change.
Your choices
You can ask for human-only review of your application, opt out of AI interview note-taking, request an accommodation, or ask questions about how we use AI. Contact recruiting@circleci.com. For how we handle your data, including retention, see our Privacy Policy.