| Microsoft Careers

Microsoft

Hybrid

Quick summary

Work type: Hybrid
Location: Redmond, WA
Salary: $119,800–$234,700 / yr
Posted: 36 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $181k

This role $177k

$106k most similar roles pay here $248k

This role pays more than 59% of similar roles. Most pay $155,000–$207,350 — the shaded band above. At the midpoint, this role pays about $177k versus about $181k for comparable roles.

Based on 238 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 1103 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 985 roles with salary data.

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At a glance

TL;DR · | Microsoft Careers

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As a Senior MLOps Engineer on the Azure Reliability team, you will play a pivotal role in advancing AI-powered operations by building scalable ML infrastructure that ensures autonomous, reliable, and secure cloud systems. Your daily responsibilities include creating evaluation loops for precision, recall, calibration, drift, and human-in-the-loop processes, as well as publishing dashboards and service level objectives (SLOs). You will also generalize ML solutions into repeatable frameworks and operationalize prompted classifiers at scale through orchestration, autoscaling, monitoring, and cost management. Additionally, you will conduct thorough reviews of data analysis techniques to ensure comprehensive coverage and translate strategic visions into actionable roadmaps with measurable outcomes. This role requires expertise in Python, Kubernetes, Docker, CI/CD pipelines, and cloud services like Azure or AWS, as well as a strong understanding of MLOps principles and responsible AI practices.

Skills

Python Azure Kubernetes Terraform Docker CI/CD Prometheus Grafana PostgreSQL MLOps AutoML ResponsibleAI SecurityBestPractices

What you'll do

Build evaluation loops for precision, recall, calibration, drift, and human-in-the-loop processes.
Generalize ML solutions into repeatable frameworks for scalability.
Operationalize prompted classifiers at scale with orchestration, autoscaling, monitoring, and cost controls.
Conduct thorough reviews of data analysis techniques to ensure accuracy and completeness.
Translate strategic vision into actionable roadmaps with measurable outcomes.

What we're looking for

Proven experience in building and operationalizing machine learning solutions at scale.
Deep expertise in MLOps, including evaluation loops, precision/recall analysis, and dashboard publishing.
Strong background in data analysis, summarization techniques, and process review.
Ability to translate strategic vision into actionable roadmaps with measurable outcomes.
Experience in AI governance, responsible AI practices, and policy-driven operations.

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