Principal Machine Learning Engineer – Foundation Models – Microsoft Research

The Research for Industry (RFI) initiative seeks to identify and accelerate scientific advances in technologies such as data platforms, connectivity, robotics, and artificial intelligence (AI) and bring them to bear on opportunities in a variety of industries, including retail, financial services, energy, and agri-food, and others.  

As part of Research for Industry, you will work closely with partners, customers, and field experts to enable the data-driven future of different businesses. In collaboration with scientists in Microsoft Research, our engineers in Azure invent new technologies to bring the best in cloud and AI to bear on the emerging challenges and opportunities in industry. 

In this role, we are looking for a Principal Machine Learning Engineer to help leverage the advancements in large models to accelerate simulations in industry scenarios, such as in agriculture, food, supply chain, retail, energy, manufacturing, and other verticals.  
 
We are seeking a Principal Machine Learning Engineer with expertise in machine learning and distributed systems. This individual will have a deep understanding of machine learning and will be proficient in the design, planning, and implementation of tools and technology to support AI-driven scientific research and simulations.

Required Qualifications

• Bachelor's Degree in Computer Science, Physics, Engineering, Chemistry, Mathematics or related technical discipline 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. 
• 4+ years of experience working with machine learning and large datasets. 
• Experience in machine learning frameworks, including, but not limited to, TensorFlow, PyTorch. 
• 4+ years of experience building and optimizing distributed systems and large-data applications, including those using tensor accelerators or Graphics processing units (GPUs).

Preferred Qualifications

• Master's degree or equivalent work experience in Computer Science, Physics, Engineering, Chemistry, Mathematics or a related field.  
• Familiarity with Linux and the open-source ecosystem.
• Prior experience developing high-performance scientific software. 
• Ability to design and execute an original research agenda.
• Ability to work in a multi-disciplinary team.
• Candidates need to be able to collaborate effectively with other researchers.  

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $133,600 - $256,800 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 $173,200 - $282,200 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 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.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. 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.

#Research

#RaR

• Architect, innovate, design, and implement scalable and robust solutions for machine learning involving large volumes of heterogeneous data.  
• Build and optimize distributed data processing and model building pipelines.  
• Develop and maintain tools and technologies for building, training, optimizing, scaling machine learning solutions  
• Collaborate with cross-functional teams, including scientists, researchers, and software engineers.  
• Document and share best practices across the organization.  
• Work on research projects, write papers, present at research communities.
  
• Maintain the highest standards in code quality and software design.