Job description

Overview The Maia Model Enablement team owns the end-to-end technical work required to bring frontier AI models onto Microsoft's AI accelerators and make them successful in production. Our work spans model enablement, performance optimization, serving capabilities, validation infrastructure, hardware bring-up, and cross-stack debugging. We are looking for a Senior Software Engineer who enjoys solving complex, end-to-end systems challenges at the intersection of AI models, software, and hardware. Successful engineers combine deep technical expertise, strong systems thinking, and the ability to quickly become effective in new domains while driving measurable impact in production. At Microsoft, our mission to empower every person and every organization on the planet to achieve more guides how we partner with customers to deliver trusted, impactful solutions. With a growth mindset culture, we innovate responsibly and measure success by shared progress people, teams, and customers. Join us to do meaningful work that changes the world and helps shape what’s next for everyone.  Responsibilities What You’ll Do • Bring new AI models and model architectures to Maia. • Diagnose and resolve issues across software, systems, and hardware layers. • Improve performance, scalability, and serving capabilities for production AI workloads. • Build automation, validation infrastructure, and engineering systems that accelerate model onboarding. • Partner across model, runtime, infrastructure, and hardware teams to remove obstacles and deliver production impact. How We Work • We work across the entire software stack, from model architectures and serving systems to infrastructure and hardware interactions. • The problems we solve evolve throughout the lifecycle of both models and hardware platforms. Engineers on the team regularly become effective in new domains and develop deep expertise when needed. • We rely heavily on measurement, experimentation, automation, and AI-assisted engineering workflows to move quickly and scale our impact. • We partner closely with model, software, and hardware teams to deliver production-ready solutions. Success Looks Like • A new model architecture can run correctly on Maia. • Production models achieve performance, scalability, and reliability targets. • Bottlenecks are identified and resolved regardless of where they exist in the stack. • New hardware generations become usable by model teams. • Model onboarding becomes faster, more repeatable, and less operationally intensive. Qualifications Required/Minimum Qualifications: • Bachelor’s Degree in Computer Science or a related technical field and 4+ years of technical engineering experience coding in languages such as C++, or Python, or equivalent experience. Other Requirements: • 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. Additional or Preferred qualifications: • Understanding of modern AI model architectures, inference systems, and performance characteristics. • Experience working with AI/ML frameworks, runtimes, or serving stacks such as PyTorch, ONNX Runtime, vLLM, TensorRT-LLM, or similar technologies. • Experience diagnosing and resolving complex issues across software, systems, and hardware layers. • Familiarity with performance analysis, distributed systems, accelerator architectures, or large-scale production services. • Experience enabling software on new hardware platforms, accelerators, or large-scale systems through activities such as bring-up, performance characterization, hardware-software debugging, or platform enablement. • Experience using AI-assisted engineering tools, agents, skills, and workflow automation to accelerate software development, debugging, code maintenance, investigation, and engineering productivity. • Proficiency in software development with languages such as Python, C++, or equivalent technologies. #AIInfra Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $160,200 - $261,000 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: careers.microsoft.com This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Requirements

Must-haves

What You’ll Do
Bring new AI models and model architectures to Maia.
Diagnose and resolve issues across software, systems, and hardware layers.
Improve performance, scalability, and serving capabilities for production AI workloads.
Build automation, validation infrastructure, and engineering systems that accelerate model onboarding.
Partner across model, runtime, infrastructure, and hardware teams to remove obstacles and deliver production impact.
We work across the entire software stack, from model architectures and serving systems to infrastructure and hardware interactions.
The problems we solve evolve throughout the lifecycle of both models and hardware platforms. Engineers on the team regularly become effective in new domains and develop deep expertise when needed.
We rely heavily on measurement, experimentation, automation, and AI-assisted engineering workflows to move quickly and scale our impact.
We partner closely with model, software, and hardware teams to deliver production-ready solutions.
Success Looks Like
A new model architecture can run correctly on Maia.
Production models achieve performance, scalability, and reliability targets.
Bottlenecks are identified and resolved regardless of where they exist in the stack.
New hardware generations become usable by model teams.
Model onboarding becomes faster, more repeatable, and less operationally intensive.
Bachelor’s Degree in Computer Science or a related technical field and 4+ years of technical engineering experience coding in languages such as C++, or Python, or equivalent experience.
Understanding of modern AI model architectures, inference systems, and performance characteristics.
Experience working with AI/ML frameworks, runtimes, or serving stacks such as PyTorch, ONNX Runtime, vLLM, TensorRT-LLM, or similar technologies.
Experience diagnosing and resolving complex issues across software, systems, and hardware layers.
Familiarity with performance analysis, distributed systems, accelerator architectures, or large-scale production services.
Experience enabling software on new hardware platforms, accelerators, or large-scale systems through activities such as bring-up, performance characterization, hardware-software debugging, or platform enablement.
Experience using AI-assisted engineering tools, agents, skills, and workflow automation to accelerate software development, debugging, code maintenance, investigation, and engineering productivity.
Proficiency in software development with languages such as Python, C++, or equivalent technologies.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonmlllm

Platform: eightfold
Effort: medium