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Principal Machine Learning Engineer at Microsoft
Redmond, Washington, United States -
Full Time

Start Date

Immediate

Expiry Date

03 Mar, 26

Salary

0.0

Posted On

03 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Data Analysis, Python, Java, Distributed Systems, Data Processing, ML Data Pipelines, Responsible AI, Large Language Models, Collaboration, Code Efficiency, Customer Orientation, Monitoring, Orchestration, Autoscaling, Cost Guardrails

Industry

Software Development

Description
Build evaluation loops (precision/recall, calibration, drift, human-in-the-loop) and publish dashboards/SLOs. Generalize machine learning (ML) solutions into repeatable frameworks. Operationalize prompted classifiers at scale (batch & streaming), including orchestration, autoscaling, monitoring, and cost guardrails. Conduct thorough review of data analysis and techniques used to summarize the process review and highlight areas that have been missed or need re-examining. Collaborate cross-functionally with DS, Security, and Platform to define schemas, access patterns, and governance. Independently write efficient, readable, extensible code and model pipelines. Commit to a customer-oriented focus by acknowledging customer needs and perspectives, validating customer perspectives, focusing on broader customer context, and serving as a trusted advisor. Bachelor'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 equivalent experience. 7+ years' experience writing production-quality Python or Java or Scala code. 5+ years' experience in distributed systems design and implementation of large scale data processing systems 3+ years' experience building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph Demonstrated interest in Responsible AI. Experience prompting, evaluating, and working with large language models.
Responsibilities
Build evaluation loops and publish dashboards/SLOs. Collaborate cross-functionally to define schemas, access patterns, and governance.