Responsibilities
Design and build production systems for machine learning training, evaluation, inference, and operation.
Transform machine learning models and research innovations into reliable, scalable product capabilities.
Own complex engineering projects from technical design through implementation and production deployment.
Develop infrastructure, services, and reusable components that support multiple models and workloads.
Diagnose and resolve challenges involving system performance, scalability, reliability, and efficiency.
Partner with applied scientists and software engineers to integrate modeling advances into production systems.
Contribute to architectural decisions and coordinate technical work across components and partner teams.
Incorporate security, privacy, safety, and Responsible AI considerations throughout system design and development.
Mentor other engineers and contribute to strong engineering practices across the team.
Qualifications
Required Qualifications
Bachelor's Degree in Computer Science or related technical field AND 4+ 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 5+ years of technical engineering experience, or equivalent experience.
Experience building machine learning infrastructure or platforms that support multiple models or workloads.
Experience contributing to architecture and delivering technical initiatives involving multiple components or teams.
Experience with distributed systems and large-scale machine learning workloads.
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 or pipelines in production.
Software engineering, system design, and problem-solving skills.
Experience building systems for one or more stages of the machine learning lifecycle, such as data processing, training, evaluation, or inference.
Ability to independently deliver complex technical projects and collaborate effectively across science and engineering disciplines.
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:
- 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.
