AWS GameTech Cortex is seeking an experienced Applied Scientist to join our team that specializes in full stack data solutions for games. In this role, you will drive the development and deployment of machine learning solutions that directly impact how game developers build, operate, and optimize their games on AWS. You will work at the intersection of gaming, data science, and cloud infrastructure to solve complex problems in areas such as player behavior prediction, game performance optimization, content recommendation, and anti-cheat systems.
As a senior member of the team, you will lead the design and implementation of ML models and algorithms that process massive-scale gaming data. You will collaborate with game studios, AWS service teams, and product managers to identify high-impact opportunities and translate them into scalable ML solutions. Your work will influence the gaming experiences of millions of players worldwide while helping game developers make data-driven decisions.
The ideal candidate combines deep expertise in machine learning with a passion for gaming and the ability to deliver production-ready solutions. You will mentor junior scientists, establish best practices for ML development, and contribute to the technical direction of the team.
Key job responsibilities
Design, develop, and deploy machine learning models and algorithms that solve critical problems for game developers, including player analytics, game performance optimization, content personalization, and fraud detection
Lead end-to-end ML projects from problem definition through production deployment, ensuring solutions are scalable, maintainable, and deliver measurable business impact
Analyze large-scale gaming datasets to identify patterns, extract insights, and develop predictive models that improve game operations and player experience
Collaborate with game studios and AWS service teams to understand their challenges, define requirements, and deliver ML solutions that integrate seamlessly with their workflows
Establish and promote best practices for ML development, experimentation, and deployment within the team, including model evaluation, A/B testing, and monitoring
Mentor and provide technical guidance to junior applied scientists and engineers, fostering a culture of innovation and technical excellence
Stay current with the latest research in machine learning, gaming analytics, and related fields, and apply relevant advances to solve customer problems
Communicate complex technical concepts and results to both technical and non-technical audiences, including senior leadership
Contribute to the technical strategy and roadmap for ML capabilities in AWS GameTech
About the team
AWS GameTech Cortex is an applied science & ML team, that specializes in full stack data solutions for games.
BASIC QUALIFICATIONS
- 5+ years of building machine learning models for business application experience
- 5+ years of applied research experience
- Experience with popular deep learning frameworks such as MxNet and Tensor Flow.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
PREFERRED QUALIFICATIONS
- Experience developing, deploying and managing AI products at scale
- Experience with RL, DRL, and distributed ML architectures
- Experience building ML products in Games industry
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, San Diego - 167,100.00 - 226,100.00 USD annually
USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually
