We are hiring a Software Engineer to join the Ads Engineering team, where you’ll be at the forefront of building infrastructure to bootstrap and host LLM agents and MCP servers at scale. This work is foundational to enabling intelligent, responsive systems that support next-generation ad delivery and optimization. Our team is also leading the development of Copilot solutions designed specifically for DRIs, integrating agents and MCP servers to provide real-time visibility into system health and performance. These tools empower the DRI team to maintain operational stability and proactively address issues across our systems.

In parallel, you will contribute to the evolution of the Ads serving stack that powers advertising across Bing Search, MSN, Start.com, Edge Browser shopping, and other Microsoft services. This stack is a low-latency, geo-distributed system operating at massive scale, handling complex workloads such as large-scale ML inferencing for ad ranking, real-time bidding, and a variety of ad-serving scenarios. Every millisecond counts, and the engineering challenges span from feature innovation to deep optimization across the stack.

You will own and drive significant feature areas, collaborate with top engineers across the globe, and contribute to a system that supports billions of ad impressions daily. The role demands strong technical depth, a proactive mindset, and the ability to work independently while influencing cross-team efforts. If you thrive on solving deep technical challenges and want to be part of a team that is shaping the future of large-scale intelligent infrastructure, this is the place to be.

Our Culture: Microsoft Culture

• Bachelor's Degree in Computer Science or related technical field AND technical engineering experience with coding in languages including, but not limited to C#, Python OR another modern language.
• A good grasp of how distributed systems, APIs, microservices, and databases fit together to power real-world applications.
• Some familiarity with working with Large Language Models (LLMs) — whether it’s prompt design, grounding data, embeddings, or integrating APIs.
• Strong problem-solving mindset, with the ability to dig into tricky issues across systems and data flows until you get to the root cause.
• Comfortable working and communicating across teams in different regions — you’ll be collaborating with engineers both locally and globally.

• Master's Degree in Computer Science or related technical field AND technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• Experience building MCP servers or working with agentic AI flows.
• Exposure to observability tools like Geneva, Prometheus, Grafana, or App Insights.
• Familiarity with AI developer frameworks such as LangChain, Semantic Kernel, or AutoGen.
• Background in building developer productivity tools (e.g., scaffolding, code generation, or internal platforms).
• Experience with containers and orchestration (Docker, Kubernetes).
• Understanding of business intelligence and analytics systems like Power BI, Spark, or Databricks.
• Comfort working within the Microsoft ecosystem — Azure DevOps, GitHub Actions, VS Code extensions, etc.

• Deliver secure, well-tested, and maintainable code across both the Ads serving stack and LLM/MCP infrastructure.

• Collaborate with engineering and product teams to build scalable, intelligent solutions for ad delivery and system health monitoring.

• Design and implement distributed services supporting ad ranking, bidding, and Copilot experiences for DRIs.

• Contribute to scenario and use case definitions focused on observability and intelligent agent interactions.

• Build resilient services with high availability, scalability, and robust diagnostics.

• Champion long-term architectural simplicity and maintainability across legacy and emerging systems.

• Own the full development lifecycle, including testing, deployment, and production readiness.

• Ensure modular, secure, reliable, and actively monitored components throughout the stack.