Overview

At Microsoft Copilot, we focus on building the best AI powered products in the world. We're building applied AI products designed to improve over time and we need someone to architect and build the infrastructure that makes that possible.

As an Machine Learning Operations (MLOps) Engineer, you'll build the connective tissue between our models and the real world. You're not just deploying models - you're building the systems that accelerates model improvement and drives continuous learning from production.

This is a high-impact, high-autonomy role where your infrastructure decisions directly shape product quality and our ability to iterate. If you've ever felt frustrated by the gap between ML's potential and its messy reality in production, this is your chance to close it.

The next wave of AI products won't win on model architecture alone—they'll win with robust infrastructure for continuous improvement. You'll build the infrastructure that makes our AI products genuinely intelligent, not just generative. Every system you create shortens the loop between user feedback and model improvement, directly impacting product quality and user experience.

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.

Starting January 26, 2026, MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction.

Responsibilities

Training pipelines that scale elegantly - Design and implement robust training infrastructure that handles everything from data ingestion to model versioning, making it trivial for ML engineers to experiment and deploy with confidence
The data flywheel - Build the infrastructure and product features that capture user interactions, ground truth labels, and edge cases, then automatically route them back into training loops. Turn every production interaction into a training example
Inference systems that deliver - Dive deep into model serving architecture—optimize latency, manage costs, implement intelligent caching, and build the observability needed to maintain reliability at scale
Deployment pipelines with guardrails - Create deployment systems that balance velocity with safety: automated testing, gradual rollouts, performance monitoring, and quick rollback mechanisms
Cross-functional infrastructure - Partner closely with ML engineers, platform engineers, and data scientists to build APIs and tools that enable tight, rapid feedback loops from production back to model development

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.

Preferred Qualifications

Master'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 Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
- OR equivalent experience.
Familiarity with LLM deployment patterns, vector databases, prompt management, and the unique challenges of serving foundation models
Experience working with RAG, fine-tuning pipelines, or evaluation frameworks
The ability to see beyond individual components to design holistic systems where data flows naturally from production through improvement cycles and back
Desire and preference to work at the intersection of teams, translating between ML researchers who want flexibility and engineers who need reliability

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 $158,400 - $258,000 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

Software Engineering IC5 - 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, read more about requesting accommodations.