The Principal Machine Learning Engineer will design, build, deploy, and scale production machine learning systems. This is a deeply technical, hands-on role for an engineer with a strong track record of transforming machine learning models and research innovations into reliable product capabilities.
The ideal candidate has experience building systems across the machine learning lifecycle, including data processing, model training, evaluation, inference, and production operation. They will partner closely with applied scientists and software engineers to solve complex engineering problems spanning models, data, infrastructure, and services.
As a Principal Machine Learning Engineer, this person will build critical system components while driving architecture and technical direction across broader initiatives. They will develop reusable capabilities that support multiple models and workloads, make sound tradeoffs across model quality, scalability, reliability, performance, and cost, and help raise the engineering standards of the broader team.
This role is particularly suited to someone passionate about AI safety and Responsible AI and motivated to build systems that help advanced AI capabilities operate safely, reliably, and responsibly.
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.
Responsibilities
- Design and build large-scale systems for machine learning training, evaluation, inference, and production operation.
- Transform machine learning models and research innovations into reliable, scalable product capabilities.
- Develop reusable infrastructure, services, and components that support multiple models and workloads.
- Solve complex engineering problems involving scalability, performance, reliability, and efficiency.
- Partner with applied scientists and software engineers to integrate modeling advances into production systems.
- Drive architectural decisions while remaining hands-on in the design and implementation of critical components.
- Incorporate security, privacy, safety, and Responsible AI considerations throughout system design and development.
- Provide technical direction and mentorship that raises the engineering bar across the team.
Qualifications
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.
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
Preferred Qualifications:
- Master’s or Doctorate degree in Computer Science or a related technical field, with a focus on machine learning or related areas, AND 7+ years of technical engineering experience, or equivalent experience.
- Experience building machine learning infrastructure or platforms that support multiple models, teams, or workloads.
- Experience driving architecture across complex, cross-team engineering initiatives.
- Demonstrated interest in AI safety and Responsible AI, including building systems that support the safe and responsible development or deployment of AI capabilities.
- Demonstrated experience deploying and scaling machine learning models and pipelines in production.
- Experience with machine learning systems, distributed systems, software architecture, and system design.
- Proven ability to build and operate systems that support large-scale machine learning workloads.
- Ability to solve technically ambiguous problems and personally implement critical systems while influencing broader technical direction.
Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $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, read more about requesting accommodations.
Skills Required
- Bachelor's degree in Computer Science or a related technical field, plus 6+ years of technical engineering experience with coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
- Ability to pass the Microsoft Cloud Background Check upon hire or transfer and every two years.
- Master's or Doctorate degree in Computer Science or a related technical field focused on machine learning or a related area, plus 7+ years of technical engineering experience, or equivalent experience.
- Experience building machine learning infrastructure or platforms supporting multiple models, teams, or workloads.
- Experience driving architecture across complex, cross-team engineering initiatives.
- Demonstrated interest in AI safety and Responsible AI.
- Experience deploying and scaling machine learning models and pipelines in production.
- Experience with machine learning systems, distributed systems, software architecture, and system design.
- Experience building and operating systems supporting large-scale machine learning workloads.
- Ability to solve technically ambiguous problems, implement critical systems, and influence broader technical direction.
Microsoft Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.
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Healthcare Strength — Health coverage is described as comprehensive, with preventive care coverage, vision options, and annual out-of-pocket limits. Feedback suggests medical, mental-health, and disability support are a major strength of the package.
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Parental & Family Support — Family support is expansive, including parental leave, family caregiver leave, child-care assistance, family concierge services, fertility options, and college-advising support for eligible employees. Feedback suggests these benefits meaningfully ease caregiving and family planning demands.
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Wellbeing & Lifestyle Benefits — Wellbeing resources include counseling and employee-assistance services, wellness reimbursements, product discounts, and charitable-matching programs. Feedback suggests these extras complement core benefits and support day-to-day quality of life.
Microsoft Insights
What We Do
At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. Our mission is grounded in both the world in which we live and the future we strive to create. Today, we live in a mobile-first, cloud-first world, and the transformation we are driving across our businesses is designed to enable Microsoft and our customers to thrive in this world.
