Member of Technical Staff- Machine Learning Operations Engineer

CLICK HERE TO APPLYAt 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 / What you'll build:

• 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

Required Qualifications:

• Doctorate in Computer Science, Statistics, Software Engineering, or related field AND 3 year(s) applied ML engineering experience
• OR Master's Degree in Computer Science, Statistics, Software Engineering, or related field AND 4 years applied ML engineering experience
• OR Bachelor's Degree in Computer Science, Data Engineering, Software Engineering, or related field AND 6 years applied ML experience,
• OR equivalent experience.
• 6 years experience building and operating ML systems in production, with real stories about what breaks at scale and how you fixed it
• 5 years of experience of software engineering fundamentals with experience in distributed systems, containerization (Docker/Kubernetes), and cloud platforms (AWS/GCP/Azure)
• 5 years of hands-on experience with ML orchestration tools (Airflow, Kubeflow, Metaflow), experiment tracking, model registries, and feature stores
• 5 years of experience optimizing model inference, wrestled with GPU utilization, and know the tradeoffs between latency, throughput, and cost

Preferred Qualifications:

• Doctorate in Computer Science, Data Engineering, Software Engineering, or related field AND 6 years data engineering experience (e.g., building ETL pipelines, managing distributed data systems, implementing data quality frameworks)
• OR Master's Degree in Computer Science, Data Engineering, Software Engineering, or related field AND 8 years data engineering experience (e.g., building ETL pipelines, managing distributed data systems, implementing data quality frameworks)
• OR Bachelor's Degree in Computer Science, Data Engineering, Software Engineering, or