Principal Software Engineer

Microsoft • Redmond, WA

34d • Hybrid

About The Position

Monetization Engineering is responsible for building a unified, intelligent, and resilient monetization platform that drives revenue across Microsoft’s AI-native surfaces, including Copilot, Search, MSN, Shopping, and both first-party and third-party ecosystems. Our mission is to enhance advertiser value, optimize platform performance, and achieve long-term revenue growth through large-scale systems, machine learning-driven optimization, experimentation, and cross-surface innovation.
The Ads Brain team serves as the technological core of Microsoft's rapidly expanding digital advertising business. The team focuses on accelerating Microsoft’s large-scale deep learning inference for Ads, Shopping, Copilot, and other surfaces, including both offline and online applications that support OpenAI LLM models and next-generation LLMs/SLMs. We play a pivotal role in bridging state-of-the-art GPU and deep learning technologies with critical business applications.
We are seeking an experienced professional with expertise in GPU inference optimization and a deep understanding of LLM/SLM architecture to join our team. This is a unique opportunity to contribute to cutting-edge advancements in AI and deep learning while driving impactful solutions for Microsoft’s advertising and monetization platforms.
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.

Requirements

• Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

• Experience with model compression (quantization, distillation, SVD, low‑rank methods).
• Experience in building high‑throughput inference serving stacks (continuous batching, KV‑cache optimizations, routing).
• Familiarity with Microsoft’s DLIS, Talon routing, Triton/TensorRT‑LLM stack, and Azure/H100/A100 GPU environments.
• Publications, competition wins, or real‑world deployments related to model efficiency.
• Solid experience in GPU inference optimization (CUDA, TensorRT, Triton, or custom GPU kernels).
• Proficiency in profiling tools (Nsight, TensorBoard, PyTorch profiler) and ability to identify CPU/GPU bottlenecks.
• Deep understanding of LLM/SLM architectures (attention, embeddings, MoE, decoders).
• Experience optimizing latency‑critical online services.
• Master's Degree in Computer Science or related technical field AND 8+ years 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 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Responsibilities

• Engage directly with key partners to understand, design, and implement complex inferencing capabilities for state-of-the-art deep learning models, driving innovations in AI infrastructure.
• Work with cutting-edge hardware and software stacks to deliver best-in-class inference performance while optimizing for cost, leveraging open-source projects to advance deep learning applications.
• Collaborate with external and internal teams to identify new areas for improvement and contribute to innovations that enhance model performance and deployment. Discover/solve impactful technical problems, advance state-of-the-art technologies, and translate ideas into production.
• Developing internal tools to support the AI lifecycle, including experiment tracking, model versioning, and performance monitoring.
• Create deep connections within our communities, focus on increasing representation, retaining, and growing our current team members, while fostering awareness and growth through an inclusive environment.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees