Principal Software Engineer

As Microsoft continues to push the boundaries of AI, we are on the lookout for passionate individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is bold and broad — to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. It’s also inclusive: we aim to make AI accessible to all — consumers, businesses, developers — so that everyone can realize its benefits. 

Microsoft AI (MAI) is looking for a talented and experienced Machine Learning Engineer to join our Search team and help shape the next generation of Visual Search. This role focuses on optimizing user engagement, retention, and personalization with innovative AI solutions, with a preference for expertise in recommendation systems and feed algorithms. However, we also welcome candidates with broader machine learning experience and a passion for solving dynamic AI challenges. 

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, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.

• Optimize and Deploy Models: Work on machine learning models for high-performance ranking and recommendation systems and Image Understanding, Computer vision. Candidates without direct experience in recommendations and ranking are still encouraged to apply if they possess exceptional technical skills in other areas of machine learning. 
• Large Language Model Expertise: Leverage large language models (LLMs) to create scalable, intelligent solutions for content understanding, user engagement, and relevance ranking. 
• Experimentation and Analysis: Drive data-driven experimentation using A/B testing, advanced analytics, and statistical techniques to identify growth opportunities and refine algorithms. 
• Infrastructure Optimization: Develop and optimize pipelines, tools, and infrastructure to support real-time decision-making, personalization, and predictive analytics. 
• Technical Leadership: Mentor team members and foster collaboration within cross-functional teams, including engineers, product managers, and designers. 
• Continuous Innovation: Stay informed on emerging trends in AI and machine learning, and integrate them to drive innovation and improve product offerings. 
• Cross-functional Collaboration: Articulate findings and recommendations to technical and non-technical audiences, influencing decisions across teams and leadership. 
• Embody our Culture and Values. 

Required:

• 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.

Preferred:

• Master'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 Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
• 3+ years of experience building and deploying ML models in production environments. 
• Solid coding skills in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow). 
• Familiarity with data processing tools (e.g., Spark, Pandas) and cloud platforms (e.g., Azure, AWS). 
• Experience with classification, recommendation, or personalization systems. 
• Experience using large language models (LLMs) for machine learning and AI applications. 
• Hands-on experience in growth engineering, driving improvements in user acquisition, engagement, and retention.  
• Experience with distributed computing frameworks (Kubernetes, Spark).
• Experience improving system performance, availability, and scalability.
• Expertise in personalization strategies and user behavior modeling. 
• Solid problem-solving skills and the ability to independently design solutions to complex challenge