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.
\nOur 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.
\nThis role focuses on advancing the performance, efficiency, and scalability of the next generation of model serving and inference platforms for Ads.
\nAs 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.
\nYou’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.
\nThis 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.
\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:
\nDesign 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.
\nArchitect 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).
\nProfile 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.
\nOwn 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.
\nCollaborate 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.
\nQualifications:
\nRequired Qualifications:
\nBachelor’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.
\nPreferred Qualifications:
\nMaster’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.
\nIndustry experience in advertising or search engine backend systems, such as large-scale ad ranking, real-time bidding (RTB), or relevance-serving infrastructure.
\nHands-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.
\nFamiliarity with LLM inference optimization,model sharding, tensor/kv-cache parallelism, paged attention, continuous batching, quantization (AWQ/FP8), and hybrid CPU–GPU orchestration.
\nDemonstrated success operating large-scale systems with SLA-based capacity forecasting, autoscaling, and performance telemetry; proven leadership in cross-functional architecture initiatives and technical mentorship.
\nPassion 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.
\nDeep 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.
\nSolid 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.
\nProven 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.
\nExpertise 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.
\n#MicrosoftAI Software Engineering IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 – $304,200 per year.
\nCertain roles may be eligible for benefits and other compensation.
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\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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\nAs a Senior AI Infrastructure Engineer focused on model training and inference, you will:
\nImplement and scale training pipelines for large transformer and LLM models, from data ingestion and preprocessing through distributed training and evaluation.
\nBuild and optimize inference services that deliver low-latency, high-reliability experiences for our customers, including autoscaling, routing, and fallbacks.
\nWork on GPU-level performance: tuning kernels, improving utilization, and identifying bottlenecks across our training and inference stack.
\nCollaborate closely with ML scientists to implement cutting edge training and inference methods and bring them to production.
\nPlay an active role in hiring, mentoring, and developing other engineers on the team.
\nRaise the bar for technical standards, reliability, and operational excellence across Intercom’s AI platform.
\nWe’re looking to hire Senior+ AI Infrastructure Engineers. You’re likely a great fit if:
\nYou have 5+ years of experience in software engineering, with a strong track record of shipping high-quality products or platforms.
\nYou hold a degree in Computer Science, Computer Engineering, or a related field (or you have equivalent experience with very strong fundamentals).
\nYou have hands-on experience with one or more of the following:
\nModel training (especially transformers and LLMs).
\nModel inference at scale (again, especially transformers and LLMs).
\nLow-level GPU work, such as writing CUDA or Triton kernels.
\nComfortable working in production environments at meaningful scale (traffic, data, or organizational).
\nYou communicate clearly, can explain complex technical topics to different audiences, and enjoy close collaboration with both engineers and non-engineers.
\nYou take pride in strong technical fundamentals, love learning, and are willing to invest in your own development.
\nHave deep knowledge of at least one programming language (for example Python, Ruby, Java, Go, etc.). Specific language experience is less important than your ability to write clean, reliable code and learn new stacks quickly.
\nWe are a well-treated bunch, with awesome benefits! If there’s something important to you that’s not on this list, talk to us!
\nCompetitive salary, annual bonus and equity
\nRegular compensation reviews - we reward great work!
\nUnlimited access to Claude Code and best-in-class AI tools; experimentation & building is encouraged & celebrated.
\nGenerous paid time off above statutory minimum
\nHybrid working
\nMacBooks are our standard, but we also offer Windows for certain roles when needed.
\nFun events for employees, friends, and family!
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\nYou'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:
\nWe're looking for someone with expertise in accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch), and experience with balancing research exploration with engineering implementation.
\nStrong candidates may also have experience with reinforcement learning, porting ML workloads between different types of accelerators, and familiarity with LLM training methodologies.
\nThe annual compensation range for this role is $350,000-$850,000 USD.
\nPlease note that we're an extremely collaborative group, and we value communication skills. The easiest way to understand our research directions is to read our recent research.
\nWe offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
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\nYou'll shape the future of fal's inference engine and ensure our generative models achieve best-in-class performance. Your work directly impacts our ability to rapidly deliver cutting-edge creative solutions to users, from individual creators to global brands.
\nDay-to-day, you'll set technical direction, guide your team to build high-performance inference solutions, and personally contribute to critical inference performance enhancements and optimizations. You'll collaborate closely with research & applied ML teams, influence model inference strategies and deployment techniques, and drive advanced performance optimizations.
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\nWe're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
\nWe believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science.
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\nOpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI.
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\nAs an Engineer on our hardware optimization and co-design team, you will co-design future hardware from different vendors for programmability and performance. You will work with our kernel, compiler and machine learning engineers to understand their unique needs related to ML techniques, algorithms, numerical approximations, programming expressivity, and compiler optimizations. You will evangelize these constraints with various vendors to develop and influence future hardware architectures towards efficient training and inference on our models. If you are excited about efficiently distributing a large language model across devices, dealing with and optimizing system-wide/rack-wide networking bottlenecks and eventually tailoring the compute pipe and memory hierarchy of the hardware platform, simulating workloads at different abstractions and working closely with our partners, this is the perfect opportunity!
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\nOur Inference team brings OpenAI’s most capable research and technology to the world through our products. We empower consumers, enterprises and developers alike to use and access our state-of-the-art AI models, allowing them to do things that they’ve never been able to before. We focus on performant and efficient model inference, as well as accelerating research progression via model inference.
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\nNice to Have:
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\nOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
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