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.

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.

Responsibilities:

Serves as the technological core of Microsoft’s rapidly expanding digital advertising business.

Focus 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.

Play a pivotal role in bridging state-of-the-art GPU and deep learning technologies with critical business applications.

Qualifications:

Required Qualifications:

Bachelor’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.

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.

Preferred Qualifications:

Master’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.

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.

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.

#MicrosoftAI

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Location

San Francisco

Employment Type

Full time

Department

Scaling

Compensation

• $295K – $555K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

More details about our benefits are available to candidates during the hiring process.

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.

About the Team

Our 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.

About the Role

We’re hiring engineers to scale and optimize OpenAI’s inference infrastructure across emerging GPU platforms. You’ll work across the stack - from low-level kernel performance to high-level distributed execution - and collaborate closely with research, infra, and performance teams to ensure our largest models run smoothly on new hardware.

This is a high-impact opportunity to shape OpenAI’s multi-platform inference capabilities from the ground up with a particular focus on advancing inference performance on AMD accelerators.

In this role, you will:

• Own bring-up, correctness and performance of the OpenAI inference stack on AMD hardware.

• Integrate internal model-serving infrastructure (e.g., vLLM, Triton) into a variety of GPU-backed systems.

• Debug and optimize distributed inference workloads across memory, network, and compute layers.

• Validate correctness, performance, and scalability of model execution on large GPU clusters.

• Collaborate with partner teams to design and optimize high-performance GPU kernels for accelerators using HIP, Triton, or other performance-focused frameworks.

• Collaborate with partner teams to build, integrate and tune collective communication libraries (e.g., RCCL) used to parallelize model execution across many GPUs.

You can thrive in this role if you:

• Have experience writing or porting GPU kernels using HIP, CUDA, or Triton, and care deeply about low-level performance.

• Are familiar with communication libraries like NCCL/RCCL and understand their role in high-throughput model serving.

• Have worked on distributed inference systems and are comfortable scaling models across fleets of accelerators.

• Enjoy solving end-to-end performance challenges across hardware, system libraries, and orchestration layers.

• Are excited to be part of a small, fast-moving team building new infrastructure from first principles.

Nice to Have:

• Contributions to open-source libraries like RCCL, Triton, or vLLM.

• Experience with GPU performance tools (Nsight, rocprof, perf) and memory/comms profiling.

• Prior experience deploying inference on other non-NVIDIA GPU environments.

• Knowledge of model/tensor parallelism, mixed precision, and serving 10B+ parameter models.

About OpenAI

OpenAI 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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