At Xbox Emerging Technologies, we are exploring new emerging technology trends to craft the next era of gaming. We are venturing beyond the horizon and charting a course forward with players and creators at the center.

Our goal is to define the future of Xbox by advancing our mission of bringing joy and community to every player on the planet. If you value dynamic and agile teams that are proactively advocating for a diverse workforce, we have a great role for you.

The Xbox Gaming AI team is seeking an experienced Senior Director, Applied Sciences to advance AI and machine learning innovations for Xbox games and platform experiences. This role will manage a team of applied & data scientists developing new deep learning and foundation models to support gaming needs. The role will connect with teams across game studios and platform services, co-creating opportunities for new player experiences with developments in AI and Machine Learning, to bring to life the next generation of platform and game experiences.

Qualifications

Required/Minimum Qualifications

• Bachelor's Degree in Statistics, Econometrics, Computer Science, or Computer Engineering, Machine Learning, Mathematics, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research, deep learning, reinforcement learning
• OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 3+ years people management experience.

Additional or Preferred Qualifications

• Master's Degree in Statistics, Econometrics, Computer Science, Computer Engineering, Machine Learning, Mathematics, or related field AND 12+ years related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 8+ years related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 7+ years people management experience.
• 3+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
• 7+ years experience conducting research as part of a research program (in academic or industry settings).
• 5+ years experience developing and deploying live production systems, as part of a product team.
• 7+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.

Applied Sciences M6 - The typical base pay range for this role across the U.S. is USD $158,500 - $276,600 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 $202,800 - $304,200 per year.

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

Responsibilities

This role requires deep technical and research expertise in machine learning, including multi-modal intelligence, language models, embodied AI, world generation, reinforcement learning, and the ability to go from theoretical approach to tangible, robust outcomes. The ideal candidate is highly collaborative and has an exceptional ability to bring ideas to life working with software engineers, applied scientists, game developers, designers, and world-class machine learning researchers. Storytelling through compelling presentations and prototypes are essential, as well as being at ease presenting to C-level executives, collaborating with game development teams, or participating in deeply technical discussions with research leaders. As with any role focused on incubation and innovation, sound judgement and ability to balance trade-offs in opportunity cost is vital.