AI Engineer

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We are looking for a creative AI Engineer who will collaborate with software engineers, researchers, and product managers to express product needs as well defined machine learning problems, push the state of the art in LLMs and bring prototypes all the way to planet scale production. You’ll build the engineering that makes AI work in production: you will build APIs, platforms and services around AI features; design data pipelines and feedback loops; deploy and fine tune state of the art deep learning models; orchestrate prompts and tools; and monitor AI specific signals—such as drift, hallucinations, safety and cost—alongside traditional service reliability metrics.

Join us to empower people through AI, bringing cutting edge deep learning into everyday work at scale. At Microsoft, our mission is to empower every person and every organization to achieve more; we show up with a growth mindset, innovate to empower others, and collaborate to realize shared goals. Rooted in respect, integrity, and accountability, we foster an inclusive culture where everyone can thrive. If you are passionate about applying state of the art AI to positively impact millions, this role is for you.

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.

Required Qualifications:

• Bachelor's Degree in Computer Science, or related technical discipline with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • OR equivalent experience.
• AI and machine learning skills. Hands on experience with generative AI or machine learning frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and a solid understanding of LLM concepts, embeddings, and prompt engineering.
  
• Distributed systems and cloud experience. Familiarity with Azure services, micro services architectures, and distributed storage systems (experience with Cosmos DB or similar is a plus). Ability to design and run fault tolerant infrastructure on a large scale and to implement secure, compliant solutions.

Other Requirements:
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:

• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Preferred Qualifications:

• 1 + years of experience designing and running infrastructure systems that operate globally on a large scale.
• Experience deploying AI models into production environments and building systems that monitor and retrain models automatically.
• Passion for mentoring others and contributing to an inclusive team culture.

Software Engineering IC2 - The typical base pay range for this role across the U.S. is USD $84,200 - $165,200 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 $109,000 - $180,400 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. Microsoft will accept applications for the role until 11/18/2025.

• Ship features with PM & Engineering. Co own scenario goals; translate product requirements into scientific plans and productionized solutions that meet quality/latency/cost targets.
• Generative AI and advanced technologies. Apply knowledge of generative AI, large language models, and modern frameworks to develop intelligent features and automation within the service.
• Azure platform integration. Deploy, integrate, and manage AI powered solutions within the Azure ecosystem, ensuring security, scalability, and compliance with best practices.
• Continuous learning and mentoring. Stay current with advances in generative AI and software engineering. Invest time in learning new tools and frameworks, propose improvements to build processes, and share knowledge with colleagues.