Anysphere에서 ML 데이터 시스템을 구축할 엔지니어를 찾습니다. Spark와 Ray Data를 활용한 대규모 데이터 파이프라인 운영 경험이 필수입니다. 데이터 모델링과 시스템 설계에 대한 깊은 이해를 바탕으로, 제품의 신뢰성과 효율성을 높이는 핵심 역할을 수행하게 됩니다. 현장 근무(Onsite) 기반의 소규모 팀에서 기술적 성장을 함께할 분을 환영합니다.
Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
Cursor ships daily. Every release leaves signals behind: telemetry, prompts, completions, agent runs, sessions. Those signals power model improvement, evals, and experimentation. Data infrastructure is what turns them into something teams can trust.
A lot of systems here started simple so we could move fast. Over time, the constraints change and the “good enough” version becomes the bottleneck. This role owns the full ladder: patch what should be patched, redesign what should be redesigned, ship the replacement, and operate it.
Privacy guarantees are part of correctness. What we can retain and use depends on Privacy Mode and org configuration, and getting that wrong breaks a product promise. We choose work by business impact: what blocks product and model teams today, and what will block them next month.
A core pipeline started as a pragmatic reuse of infrastructure built for something else. It works, but it cannot guarantee properties downstream consumers now need (for example, point-in-time consistency). You design and ship the replacement while keeping the existing system running.
A new product surface ships without instrumentation. You talk to the team, define what needs to be captured, and wire it through before the absence becomes anyone else’s problem.
Eval coverage drops. You trace it to an instrumentation gap introduced weeks ago by a product change nobody flagged. You fix the gap, add a contract so it cannot recur, and ship the dashboard that would have caught it earlier.
Multiple consumers depend on overlapping data. You design schema evolution and validation so changes in one place do not silently degrade the others.
Storage costs rise faster than usage. You decide what is worth keeping, implement retention and compression, and delete what is not.
We’re looking for someone who has built real systems at scale and cares about correctness, cost, and ergonomics.
Strong signals include:
Deep experience with Spark (Databricks or open-source Spark both count)
Production experience with Ray Data
Hands-on ownership of large data pipelines and storage systems
Comfort debugging performance issues across client instrumentation, streaming, storage, and model-facing workflows, as well as, compute, storage, and networking layers
Clear thinking about data modeling and long-term maintainability
You have good judgment about when to patch and when to rebuild
Nice to have
Experience running or scaling ClickHouse
Familiarity with dbt, Dagster, or similar orchestration and modeling tools
We're in-person with cozy offices in North Beach, San Francisco and Manhattan, New York, replete with well-stocked libraries.
If there appears to be a fit, we'll reach to schedule 2-3 short technicals. After, we'll schedule an onsite in our office, where you'll work on a small project, discuss ideas, and meet the team.
