软件工程师，智能体评估与质量
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Software Engineer, Agent Evaluation and QualityCursor (Anysphere) · San Francisco; New York
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职位描述
机器翻译我们的使命是实现编码自动化。我们旅程的第一步是为专业程序员打造最好的工具，结合富有创造力的研究、设计和工程。我们的组织非常扁平，团队规模小且人才密集。我们尤其喜欢求真、充满热情且富有创造力的人。我们享受激烈的辩论、疯狂的想法和交付代码。
岗位职责
作为 SpaceXAI 智能体质量团队的软件工程师，你将构建测量、评估和反馈循环基础设施，使 Cursor 核心智能体随时间推移可靠地变得更好。 这个角色位于产品、数据和工程的交叉点：你将埋点测量重要的事项，帮助定义我们如何评判质量，构建管道和工具以大规模分析智能体行为，并与研究、产品和基础设施团队紧密合作，将洞察转化为改进。 你的影响将在基于共享框架构建的每一个 Cursor 产品中叠加——并在围绕模型选择、质量和成本的高风险决策中叠加。 你将从事的工作 • 设计和构建一流的 AI 评估系统：精选数据集、离线回放、评分器/评判器、回归告警和仪表盘。
• 设计来自真实使用的反馈循环：收集、清洗和解读用户信号，为模型和框架变更提供依据。
• 开发用于调试智能体行为的分析工具和工作流：深入探究失败模式、聚类主题，并呈现可执行的洞察。
• 通过使质量可衡量和可运营来提升可靠性和护栏：定义“好/坏/降级”会话、告警和分诊原语。
你可能适合，如果 • 你曾构建和运营评估或测量系统，例如 AI 评估、实验、排名/相关性或搜索质量。你能将模糊的“质量”问题转化为具体的指标、管道和决策。
• 你拥有强大的数据敏锐度，并能与数据科学家和研究人员有效协作。
• 你对模型和智能体行为有品味和强烈观点。你持续关注并了解新兴研究和行业趋势。
• 你拥有扎实的软件工程基础，并享受交付生产系统。
申请 如果看起来合适，我们会联系你安排 2-3 场简短的技术面试。之后，我们会安排一次在我们办公室的现场面试，你将在那里完成一个小项目、讨论想法并认识团队。 #LI-DNI
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职位描述
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.
岗位职责
As a Software Engineer on the Agent Quality team at SpaceXAI, you’ll build the measurement, evaluation, and feedback-loop infrastructure that makes the Cursor core agent reliably better over time. This role sits at the intersection of product, data, and engineering: you’ll instrument what matters, help define how we judge quality, build pipelines and tooling to analyze agent behavior at scale, and partner closely with research, product, and infrastructure teams to turn insights into improvements. Your impact will compound across every Cursor product built on the shared harness—and across high-stakes decisions around model choice, quality, and cost. What you’ll work on • Designing and building best-in-class AI evaluation system: curated datasets, offline replay, scorers / judges, regression alerts, and dashboards.
• Designing feedback loops from real usage: collecting, cleaning, and interpreting user signals to inform model and harness changes.
• Developing analysis tooling and workflows for debugging agent behavior: deep dives on failure modes, clustering themes, and surfacing actionable insights.
• Improving reliability and guardrails by making quality measurable and operational: defining “good/bad/degraded” sessions, alerting, and triage primitives.
You may be a fit if • You’ve built and operated evaluation or measurement systems, such as AI evals, experimentation, ranking/relevance, or search quality. You can turn ambiguous “quality” questions into concrete metrics, pipelines, and decisions.
• You have strong data acumen, and can collaborate effectively with data scientists and researchers.
• You have taste and strong opinions on model and agent behaviors. You stay up-to-date and informed on emerging research and industry trends.
• You have strong software engineering fundamentals and enjoy shipping production systems.
Applying 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. #LI-DNI