Member of Technical Staff, AI - Pre-Training

Help deliver one of the best foundational models in the world at Microsoft AI. 

At Microsoft AI, we are on a mission to train the world’s most capable AI frontier models, pushing the boundaries of scale, performance and product deployment. The Pre-Training team at Microsoft AI tackles some of the most challenging problems in deep learning at scale. As a team, we will deliver one of the best foundation models in the world, forming the foundation of many initiatives across Microsoft AI. 

We are looking for outstanding individuals excited about contributing to the next generation of systems that will transform the field. We are looking for candidates who: 

• Have proven expertise in areas of interest, evidenced by an exceptional publication track record and significant technical leadership in high-impact projects 

• Exhibit strong analytical skills, attention to detail, and a commitment to data-driven decision-making 

• Have experience and/or in-depth understandings about large-scale distributed systems 

• Demonstrate an ability to work collaboratively in a fast-paced, innovative environment

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, Machine Learning, Mathematics, or related technical discipline AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python 
  • OR equivalent experience. 

Preferred Qualifications 

• Bachelor's Degree in Computer Science or related technical field AND 10+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python 
  • OR 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 equivalent experience. 
• Demonstrated experience in large-scale AI. 
• Passionate about conversational AI and its deployment. 
• Demonstrated written and verbal communication skills with the ability to work closely with cross-functional teams, including product managers, designers, and other engineers.   
• Passion for learning new technologies and staying up to date with industry trends, best practices, and emerging technologies in AI.  
• Proven ability to collaborate and contribute to a positive, inclusive work environment, fostering knowledge sharing and growth within the team.  

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $117,200 - $229,200 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 $153,600 - $250,200 per year.

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $137,600 - $267,000 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 $180,400 - $294,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

Microsoft will accept applications and processes offers for these roles on an ongoing basis.

• Develop algorithms, model architectures, data mixtures, and scaling laws for large-scale training using a rigorous data-driven approach grounded in meticulous ablations  

• Drive algorithmic implementations, conduct experiments, and oversee flagship training runs on our in-house large-scale distributed stack  

• Collaborate closely with teams on infrastructure, data, post-training, and multimodality 

• Embody our and .