As a Software Engineer on the Agent Quality team at Cursor, 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 behaviour 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 behaviour: 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 behaviours. You stay up-to-date and informed on emerging research and industry trends.

• You have strong software engineering fundamentals and enjoy shipping production systems.

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We’re looking for a Data Infrastructure Manager to lead a team of talented engineers building and scaling the data infrastructure that powers Microsoft’s consumer AI. This role sits at the intersection of technical leadership and people management. You’ll set the technical direction for large-scale data and ML pipelines, AI agentic workflows, and intelligent systems while growing a high-performing team of ICs.

If you’ve architected big data platforms from the ground up and are now ready to multiply your impact through others, including on some of the most exciting AI infrastructure challenges in the industry, we want to hear from you.

Deep technical expertise in big data and distributed systems A track record of leading and developing engineering talent A passion for automation, observability, and operational excellence The ability to translate complex technical strategy into clear, executable plans Empathy, collaboration, and a growth mindset

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of Respect, Integrity, and Accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

Team Leadership & People Development Hire, mentor, and develop a team of Data Infrastructure Engineers, fostering a culture of technical excellence, ownership, and continuous growth. Conduct regular 1:1s, set clear goals, and provide actionable feedback to support each engineer’s career development. Build and sustain an inclusive, collaborative team environment aligned with Microsoft’s values of Respect, Integrity, Accountability, and Inclusion.

Technical Strategy & Architecture Define and drive the technical vision for a scalable, reliable, and observable Big Data Infrastructure serving mission-critical AI applications, including agentic and intelligent systems. Lead technical design reviews, establish engineering standards, and ensure a clean, secure, and well-documented codebase. Partner with engineers to architect data solutions across storage, compute, and analytics layers, including the pipelines and orchestration frameworks that underpin AI agent workflows, balancing long-term scalability with near-term delivery.

Platform & Operations Champion DevOps and SRE best practices across the team, including automated deployments, service monitoring, and incident response. Guide the team in building a self-service big data platform that empowers data engineers, researchers, and partner teams. Oversee robust CI/CD pipelines and infrastructure-as-code practices using tools like Bicep, Terraform, and ARM. Lead capacity planning and drive proactive resolution of bottlenecks in data pipelines and infrastructure.

Cross-Functional Collaboration Act as a key technical partner to Data Engineers, Data Scientists, AI Researchers, ML Engineers, and Developers to deliver secure, seamless big data workflows. Collaborate with Security teams to uphold strong infrastructure security practices (IAM, OAuth, Kerberos). Represent the team in planning and prioritization discussions, translating organizational goals into actionable engineering roadmaps.

Qualifications Bachelor’s Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling or data engineering work OR Master’s Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, or data engineering work OR equivalent experience.

Preferred Qualifications Master’s Degree in Computer Science or related technical field AND 10+ years of technical engineering experience OR Bachelor’s Degree AND 14+ years, OR equivalent experience. 5+ years in Big Data Infrastructure, DevOps, SRE, or Platform Engineering. 5+ years of hands-on experience with distributed systems from bare-metal to cloud-native environments. 5+ years overseeing or contributing to containerized application deployments using Kubernetes and Helm/Kustomize. Solid scripting and automation fluency in Python, Bash, or PowerShell. Proven track record managing CI/CD pipelines, release automation, and production incident response. Hands-on expertise with modern data platforms like Databricks, including deep familiarity with relational and NoSQL databases, key-value stores, Spark compute engines, distributed file systems (e.g., HDFS, ADLS Gen2), and messaging systems (e.g., Event Hub, Kafka, RabbitMQ). Proven experience with cloud-native infrastructure across Azure, AWS, or GCP. Strong collaboration history with Data Engineers, Data Scientists, ML Engineers, Networking, and Security teams. Experience with agentic workflow infrastructure, including orchestration frameworks (e.g., Semantic Kernel, AutoGen), retrieval pipelines, and the data infrastructure patterns that support multi-agent systems at scale. Familiarity with modern web stacks: TypeScript, Node.js, React, and optionally PHP.

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