Software Engineering Manager Jobs at Microsoft with Visa Sponsorship
Microsoft hires Software Engineering Managers across its product, cloud, and AI divisions, with a well-established process for sponsoring work visas at this level. If you're targeting a leadership engineering role here, the company has a consistent track record of supporting international candidates through the full sponsorship process.
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Overview
As a Principal Software Engineering Manager - AI Frameworks on the team, you will lead and grow a group of engineers working across multiple layers of the AI software serving stack, including fundamental abstractions, runtimes, libraries, and application programming interfaces (APIs). You will be responsible for setting technical direction, prioritizing investments, and ensuring the team delivers high-impact performance improvements that enable large-scale model training and inference.
In this role, you will guide the team’s work on benchmarking OpenAI and other large language models (LLMs) across GPUs and Microsoft hardware, driving performance optimization, monitoring regressions, and accelerating time-to-deployment. You will partner closely with researchers, product teams, and platform owners to translate performance insights into production-ready improvements that reduce hardware footprint and support Microsoft Azure’s capex efficiency goals.
Responsibilities
- Lead and develop a team of engineers working across multiple layers of the AI software stack to enable large-scale training and inference.
- Set technical vision and execution strategy for model performance benchmarking, optimization, and deployment across GPUs and Microsoft hardware.
- Drive performance outcomes by prioritizing and overseeing efforts to benchmark, profile, debug, and optimize training and inference workloads.
- Own performance health by establishing mechanisms to monitor regressions, measure impact, and continuously improve time-to-deploy and hardware efficiency.
- Partner cross-functionally with research, product, infrastructure, and hardware teams to deliver scalable, production-ready AI performance improvements.
- Balance short-term delivery and long-term investments, ensuring the team’s work aligns with organizational goals, platform roadmaps, and Azure capex objectives.
- Build a strong engineering culture through coaching, feedback, hiring, and career development, enabling the team to operate with increasing autonomy and impact.
Qualifications Minimum/Required Qualifications:
- 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.
Preferred:
- Master’s Degree in Computer Science or related technical field AND 10+ years of software engineering experience, including 6+ years in engineering management, OR Bachelor’s Degree in Computer Science or related technical field AND 12+ years of software engineering experience, including 6+ years in engineering management, or equivalent experience.
- Strong technical foundation in software engineering principles, computer architecture, GPU architecture, and hardware acceleration for neural networks, with the ability to guide teams working in these areas.
- Experience leading teams responsible for end-to-end performance analysis and optimization of LLMs, AI systems, or HPC workloads, including use of GPU profiling and performance analysis tools.
- Demonstrated ability to lead cross-team initiatives, align stakeholders, and translate research or platform capabilities into scalable, production-ready solutions.
- Proven people leadership skills, including hiring, coaching, performance management, and career development, with a track record of building high-performing, inclusive teams.
- Exposure to AI / ML infrastructure, including DNN or LLM training and/or inference systems, and experience with at least one modern deep learning framework (e.g., PyTorch, TensorFlow, ONNX Runtime).
- Familiarity with GPU software stacks and acceleration technologies such as CUDA, ROCm, Triton, or equivalent, sufficient to guide technical direction and evaluate tradeoffs.
Compensation
- 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.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
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.

Overview
As a Principal Software Engineering Manager - AI Frameworks on the team, you will lead and grow a group of engineers working across multiple layers of the AI software serving stack, including fundamental abstractions, runtimes, libraries, and application programming interfaces (APIs). You will be responsible for setting technical direction, prioritizing investments, and ensuring the team delivers high-impact performance improvements that enable large-scale model training and inference.
In this role, you will guide the team’s work on benchmarking OpenAI and other large language models (LLMs) across GPUs and Microsoft hardware, driving performance optimization, monitoring regressions, and accelerating time-to-deployment. You will partner closely with researchers, product teams, and platform owners to translate performance insights into production-ready improvements that reduce hardware footprint and support Microsoft Azure’s capex efficiency goals.
Responsibilities
- Lead and develop a team of engineers working across multiple layers of the AI software stack to enable large-scale training and inference.
- Set technical vision and execution strategy for model performance benchmarking, optimization, and deployment across GPUs and Microsoft hardware.
- Drive performance outcomes by prioritizing and overseeing efforts to benchmark, profile, debug, and optimize training and inference workloads.
- Own performance health by establishing mechanisms to monitor regressions, measure impact, and continuously improve time-to-deploy and hardware efficiency.
- Partner cross-functionally with research, product, infrastructure, and hardware teams to deliver scalable, production-ready AI performance improvements.
- Balance short-term delivery and long-term investments, ensuring the team’s work aligns with organizational goals, platform roadmaps, and Azure capex objectives.
- Build a strong engineering culture through coaching, feedback, hiring, and career development, enabling the team to operate with increasing autonomy and impact.
Qualifications Minimum/Required Qualifications:
- 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.
Preferred:
- Master’s Degree in Computer Science or related technical field AND 10+ years of software engineering experience, including 6+ years in engineering management, OR Bachelor’s Degree in Computer Science or related technical field AND 12+ years of software engineering experience, including 6+ years in engineering management, or equivalent experience.
- Strong technical foundation in software engineering principles, computer architecture, GPU architecture, and hardware acceleration for neural networks, with the ability to guide teams working in these areas.
- Experience leading teams responsible for end-to-end performance analysis and optimization of LLMs, AI systems, or HPC workloads, including use of GPU profiling and performance analysis tools.
- Demonstrated ability to lead cross-team initiatives, align stakeholders, and translate research or platform capabilities into scalable, production-ready solutions.
- Proven people leadership skills, including hiring, coaching, performance management, and career development, with a track record of building high-performing, inclusive teams.
- Exposure to AI / ML infrastructure, including DNN or LLM training and/or inference systems, and experience with at least one modern deep learning framework (e.g., PyTorch, TensorFlow, ONNX Runtime).
- Familiarity with GPU software stacks and acceleration technologies such as CUDA, ROCm, Triton, or equivalent, sufficient to guide technical direction and evaluate tradeoffs.
Compensation
- 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.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
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.
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Get Access To All JobsTips for Finding Software Engineering Manager Jobs at Microsoft Jobs
Frame your leadership scope precisely
Microsoft's Software Engineering Manager interviews weight team scale and technical depth heavily. Document the number of direct reports you've managed, the systems they owned, and measurable outcomes your team delivered before you start applying.
Target divisions with active LCA filings
Microsoft sponsors across multiple business groups, but Azure, M365, and AI Platform teams file Labor Condition Applications with the DOL most consistently for engineering management roles. Prioritize open headcount in those orgs.
Use Migrate Mate to surface open roles with sponsorship history
Browse Software Engineering Manager openings at Microsoft filtered by visa type on Migrate Mate. It surfaces roles where Microsoft has actively sponsored candidates, so you're not cold-applying to positions that may not have sponsorship budget allocated.
Software Engineering Manager at Microsoft jobs are hiring across the US. Find yours.
Find Software Engineering Manager at Microsoft JobsFrequently Asked Questions
Does Microsoft sponsor H-1B visas for Software Engineering Managers?
Yes, Microsoft sponsors H-1B visas for Software Engineering Manager roles. This is one of the most common pathways for international candidates joining at this level. If you're subject to the H-1B cap, your petition would be submitted in April with an October 1 start. Microsoft also sponsors cap-exempt transfers if you're already on an active H-1B with another employer.
Which visa types does Microsoft commonly sponsor for Software Engineering Manager roles?
Microsoft sponsors H-1B, E-3, and H-1B1 visas for Software Engineering Managers, along with immigrant visa pathways including EB-2 and EB-3 for longer-term permanent residence. E-3 is available to Australian citizens and bypasses the H-1B lottery entirely. H-1B1 is available to citizens of Chile and Singapore. Which pathway applies depends on your nationality and current immigration status.
How do I apply for Software Engineering Manager jobs at Microsoft?
Applications go through Microsoft's careers portal at careers.microsoft.com. For Software Engineering Manager roles, the process typically involves a recruiter screen, a hiring manager call, and a full loop of behavioral and systems design interviews. Indicate your need for sponsorship early in the recruiter conversation. You can also browse sponsored Software Engineering Manager openings at Microsoft on Migrate Mate, which filters roles by visa type.
What qualifications does Microsoft expect for a Software Engineering Manager?
Microsoft generally expects a bachelor's degree or higher in computer science, software engineering, or a related technical field, plus hands-on prior software engineering experience before moving into management. Interviewers assess both technical depth and people leadership. At the Software Engineering Manager level, experience owning delivery across a team, driving roadmap decisions, and managing cross-functional stakeholders is weighted heavily in the loop.
How long does the sponsorship process take for a Software Engineering Manager at Microsoft?
Timeline depends on the visa type. E-3 and H-1B1 can move quickly once the Labor Condition Application is certified by the DOL, often within weeks for consular processing. Cap-subject H-1B petitions follow a fixed federal calendar with an April registration window and October 1 start date. Green Card sponsorship through PERM is a multi-year process and typically begins after you've established yourself in the role.
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