Principal Software Engineer

Microsoft•Redmond, WA

19d•Hybrid

About The Position

Microsoft Advertising is seeking a Principal Software Engineer to join our Ads Engineering Platform team and advance the core capabilities of our ad-serving infrastructure—the engine that powers advertising across Bing Search, MSN, Microsoft Start, and shopping experiences in the Edge browser. Our serving stack operates at massive global scale, delivering millions of ad requests per second through a geo-distributed, low-latency system that combines large-scale GPU/CPU inference, real-time bidding, and intelligent ranking pipelines. This role focuses on advancing the performance, efficiency, and scalability of the next generation of model serving and inference platforms for Ads.As a senior technical leader, you’ll design and optimize high-performance serving systems and GPU inference frameworks that drive measurable latency improvements and cost efficiency across Microsoft’s ad ecosystem. You’ll work across the stack—from CUDA kernel tuning and NUMA-aware threading to large-scale distributed orchestration and model deployment for deep learning and LLM workloads. This is a rare opportunity to shape the architecture of one of the world’s most advanced, mission-critical online serving platforms, collaborating with world-class engineers to deliver innovation at Internet scale. 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.

Nice To Haves

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.
Industry experience in advertising or search engine backend systems, such as large-scale ad ranking, real-time bidding (RTB), or relevance-serving infrastructure.
Hands-on experience with real-time data streaming systems (Kafka, Flink, Spark Streaming), feature-store integration, and multi-region deployment for low-latency, globally distributed services.
Familiarity with LLM inference optimization—model sharding, tensor/kv-cache parallelism, paged attention, continuous batching, quantization (AWQ/FP8), and hybrid CPU–GPU orchestration.
Demonstrated success operating large-scale systems with SLA-based capacity forecasting, autoscaling, and performance telemetry; proven leadership in cross-functional architecture initiatives and technical mentorship.
Passion for performance engineering, observability, and deep systems debugging, with a solid drive to push the limits of serving infrastructure for the next generation of ads and AI models.
Deep expertise in GPU inference frameworks such as NVIDIA Triton Inference Server, CUDA, and TensorRT, including hands-on experience implementing custom CUDA kernels, optimizing memory movement (H2D/D2H), overlapping compute and I/O, and maximizing GPU occupancy and kernel fusion for deep learning and LLM workloads.
Solid understanding of model-serving trade-offs—batching vs. streaming, latency vs. throughput, quantization (FP16/BF16/INT8), dynamic batching, continuous model rollout, and adaptive inference scheduling across CPU/GPU tiers.
Proven ability to profile and optimize GPU and system workloads—including tensor/memory alignment, compute–memory balancing, embedding table management, parameter servers, hierarchical caching, and vectorized inference for transformer/LLM architectures.
Expertise in low-level system and OS internals, including multi-threading, process scheduling, NUMA-aware memory allocation, lock-free data structures, context switching, I/O stack tuning (NVMe, RDMA), kernel bypass (DPDK, io_uring), and CPU/GPU affinity optimization for large-scale serving pipelines.

Responsibilities

Design and lead the development of large-scale, distributed online serving systems—including GPU-accelerated and CPU-based ranking/inference pipelines—to process millions of ad requests per second with ultra-low latency, high throughput, and solid reliability.
Architect and optimize end-to-end inference infrastructure, including model serving, batching/streaming, caching, scheduling, and resource orchestration across heterogeneous hardware (GPU, CPU, and memory tiers).
Profile and optimize performance across the full stack—from CUDA kernels and GPU pipelines to CPU threads and OS-level scheduling—identifying bottlenecks, tuning latency tails, and improving cost efficiency through advanced profiling and instrumentation.
Own live-site reliability as a DRI: design telemetry, alerting, and fault-tolerance mechanisms; drive rapid diagnosis and mitigation of performance regressions or outages in globally distributed systems.
Collaborate and mentor across teams—driving architecture reviews, enforcing engineering excellence, promoting system-level optimization practices, and mentoring others in deep debugging, profiling, and performance engineering.

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