Pre-Training Engineer at Microsoft AI
United States · పూర్తి సమయం
దరఖాస్తు చేసుకునే వారిలో మొదటి వ్యక్తిగా ఉండండి
- అనుభవం
- 4–8 సంవత్సరాలు
- జీతం
- USD 119,800 – USD 274,800 / year
- ఖాళీలు
- 1
- పోస్ట్ చేయబడింది
- 1 గంట క్రితం
- పని విధానం
- కార్యాలయంలో
- విద్య
- బ్యాచిలర్ డిగ్రీ
- పునఃప్రారంభం
- దరఖాస్తు చేసుకోవాలి
మీరు ఎక్కడ పని చేస్తారు
ఉద్యోగ వివరణ
Overview
Join Microsoft AI's mission to develop one of the world's leading foundational AI models. The Pre-Training team addresses complex deep learning challenges at scale, creating foundational systems that support numerous Microsoft AI initiatives.
About the Role
We seek exceptional candidates passionate about shaping the future of AI systems. Ideal applicants demonstrate strong expertise backed by a solid publication history and leadership on impactful projects, possess keen analytical thinking with dedication to data-informed decision-making, and have experience or extensive knowledge in large-scale distributed systems. Collaboration and adaptability in a fast-paced environment are essential.
Microsoft strives to empower individuals and organizations globally by fostering innovation, growth mindset, and inclusive culture emphasizing respect, integrity, and accountability.
Starting January 26, 2026, employees affiliated with Microsoft AI are expected to work onsite at a Microsoft office at least four days per week if residing within the specified distance from that location, subject to local laws.
This position is part of the Superintelligence Team within Microsoft AI, focusing on advancing ultra-capable AI systems that are controllable, safety-aligned, and grounded in human values. Our goal is to amplify human potential responsibly, producing breakthroughs that improve science, education, and societal welfare. Collaborations with key product teams ensure our models reach billions globally with beneficial impact.
Key Responsibilities
- Design and develop advanced algorithms, model structures, data blends, and scaling principles for large-scale training using a rigorous, data-centric methodology supported by detailed ablation studies.
- Implement algorithms, execute experiments, and manage flagship training operations on Microsoft’s proprietary large-scale distributed computing infrastructure.
- Work closely with teams focusing on infrastructure, data management, post-training processes, and multi-modal AI.
- Embody and promote Microsoft’s culture and values in all activities.
Qualifications
Mandatory
- Bachelor’s degree in Computer Science, Machine Learning, Mathematics, or a closely related discipline with at least four years of relevant technical engineering experience involving programming in languages like C, C++, C#, Java, JavaScript, or Python.
- Alternatively, equivalent professional experience is acceptable.
Preferred
- Bachelor’s degree plus six or more years of relevant engineering experience, or Master’s degree plus eight or more years of experience, coding in specified languages.
- Hands-on experience in large-scale AI systems.
- Diving passion and understanding of conversational AI and its practical applications.
- Strong communication skills for effective collaboration with cross-functional teams including product managers, designers, and engineers.
- Eagerness to learn emerging AI technologies and keep abreast of industry trends and best practices.
- Demonstrable ability to foster a collaborative, inclusive, and knowledge-sharing team environment.
Additional Information
The position offers a base salary range depending on the level and location: IC4 ranges from $119,800 to $234,700 annually across the U.S., with higher ranges in the San Francisco Bay Area and New York City. IC5 ranges from $142,800 to $274,800 annually nationally, with increased ranges in those metropolitan areas.
Benefits and other compensation options may be available. Microsoft is committed to equal employment opportunity for all qualified applicants, irrespective of various protected characteristics. Accommodations for disabilities and religious practices are available upon request during the application process.