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.
\nOur 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.
\nWe are seeking an experienced professional with expertise in GPU inference optimization and a deep understanding of LLM/SLM architecture to join our team.
\nThis 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.
\nMicrosoft’s mission is to empower every person and every organization on the planet to achieve more.
\nAs employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals.
\nEach 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.
\nStarting 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.
\nThis expectation is subject to local law and may vary by jurisdiction.
\nResponsibilities:
\nServes as the technological core of Microsoft’s rapidly expanding digital advertising business.
\nFocus 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.
\nPlay a pivotal role in bridging state-of-the-art GPU and deep learning technologies with critical business applications.
\nQualifications:
\nRequired Qualifications:
\nBachelor’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 equivalent experience.
\nAbility to meet Microsoft, customer and/or government security screening requirements are required for this role.
\nThese requirements include but are not limited to the following specialized security screenings:
\nMicrosoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
\nPreferred Qualifications:
\nMaster’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 Bachelor’s Degree in Computer Science or related technical field AND 15+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
\nSolid experience in GPU inference optimization (CUDA, TensorRT, Triton, or custom GPU kernels).
\nProficiency in profiling tools (Nsight, TensorBoard, PyTorch profiler) and ability to identify CPU/GPU bottlenecks.
\nDeep understanding of LLM/SLM architectures (attention, embeddings, MoE, decoders).
\nExperience optimizing latency-critical online services.
\nExperience with model compression (quantization, distillation, SVD, low-rank methods).
\nExperience in building high-throughput inference serving stacks (continuous batching, KV-cache optimizations, routing).
\nFamiliarity with Microsoft’s DLIS, Talon routing, Triton/TensorRT-LLM stack, and Azure/H100/A100 GPU environments.
\nPublications, competition wins, or real-world deployments related to model efficiency.
\n#MicrosoftAI
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