Machine Learning Engineer - II

Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit thinking in a cloud-enabled world.

The AI Platform organization at Microsoft builds the end-to-end Azure AI stack/PaaS and is core to Azure’s innovation and differentiation, as well as all of Microsoft’s flagship products, from Office to Teams, to Xbox. We are the team building Azure OpenAI, Azure ML, Cognitive Services, and the global Azure AI infrastructure for running the largest AI workloads on the planet. 

Within AI Platform, our team in Evaluation AI is working on cutting-edge Deep Learning models in NLP, Vision and Multi-modal space and building the next generation model evaluation platform.

We are looking for a passionate, creative, analytical Machine Learning Engineer who loves NLP, deep learning and wants to ship products quickly at a massive scale. We will provide a lot of opportunities for you to learn, grow and contribute.

Required Qualification:

• Master's AND 4+ years' experience, or Bachelor's AND 6+ years' experience in Computer Science, Computer Engineering, or related field with specialization in Generative AI - LLMs/natural language processing/computer vision/multimodal analysis/deep learning/machine learning/Data Science
• Strong hold on Data Structures, algorithmic problem solving and proficiency in software development/engineering skills (Python/Java/C#)
• Proficiency in Python and open-source tools (e.g., TensorFlow, PyTorch) to develop production-grade quality product
• Knowledge with Generative AI suite, transformer-based and diffuser-based models (e.g., BERT, GPT, T5, Llama, Stable diffusion) and LLMs
• Proficiency in Agile development practices and Continuous Integration/Continuous Deployment (CI/CD)
• Experience with cloud platforms (e.g., Azure, AWS) and distributed computing
• Excellent verbal and written communication skills and ability to work independently and collaboratively.

Preferred Qualifications: 

• Experience on large-scale data processing, distributed computing, or cloud platforms preferred
• Training & fine-tuning experience on large data
• Experience with building, debugging and optimizing large, high performance ML workloads preferred
• Experience with statistical analysis and data visualization tools.
• Good understanding of statistics, linear algebra, and probability theory

#IDCAIPlatformHiring

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

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.

• Build sophisticated Evaluation framework for cutting-edge deep learning models, including LLMs, SLMs, RAG, fine-tuned, distilled models and Generative AI applications such as Co-pilots, Agentic frameworks
• Develop the next generation of Azure AI's evaluation platform by integrating advanced AI evaluation solutions into production, with a strong emphasis on building a highly performant, scalable, reliable, and high-quality distributed system
• Implement State-of-the-art evaluation frameworks to assess model performance across diverse metrics, including quality, latency, cost, robustness and fairness
• Engage in sophisticated prompt engineering techniques to optimize and tailor model outputs for various real-world applications and use cases
• Build end-to-end pipelines covering model training, data analysis, model serving and model evaluation
  
• Build and Utilize CI/CD pipelines to manage the build, release, deployment, and test automation processes for AI models, ensuring seamless integration into production systems and maintaining high-quality standards
• Collaborate with cross-functional teams, including researchers, data scientists, software engineers, and product managers
• Implement new product features and evaluation metrics
  
• Embody our culture and values