Senior Machine Learning Engineer - RAI

** Overview*
* The Senior 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 experience 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.

At the Senior level, this person will independently own major components and projects, make sound technical decisions, and drive complex work from design through production. They will contribute to the architecture of broader systems, coordinate work across components or partner teams, and develop reusable solutions that improve the team's ability to build and operate machine learning capabilities.

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 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

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* Preferred Qualifications:

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* + 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.

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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…