Senior Applied Scientist, Aws Gametech Cortex

Amazon Amazon · Big Tech · Seattle, WA · Data Science

Senior Applied Scientist role focused on developing and deploying machine learning solutions for the gaming industry within AWS GameTech. The role involves full-stack data solutions, impacting game developers and player experiences through prediction, optimization, recommendation, and anti-cheat systems. Requires experience in building ML models, applied research, deep learning frameworks, and distributed systems, with a preference for AI product development at scale and RL/DRL experience.

What you'd actually do

  1. 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
  2. Lead end-to-end ML projects from problem definition through production deployment, ensuring solutions are scalable, maintainable, and deliver measurable business impact
  3. Analyze large-scale gaming datasets to identify patterns, extract insights, and develop predictive models that improve game operations and player experience
  4. Collaborate with game studios and AWS service teams to understand their challenges, define requirements, and deliver ML solutions that integrate seamlessly with their workflows
  5. Establish and promote best practices for ML development, experimentation, and deployment within the team, including model evaluation, A/B testing, and monitoring

Skills

Required

  • building machine learning models for business application experience
  • applied research experience
  • deep learning frameworks such as MxNet and Tensor Flow
  • large scale distributed systems such as Hadoop, Spark

Nice to have

  • developing, deploying and managing AI products at scale
  • RL, DRL, and distributed ML architectures
  • building ML products in Games industry

What the JD emphasized

  • building machine learning models for business application experience
  • applied research experience
  • Experience developing, deploying and managing AI products at scale
  • Experience with RL, DRL, and distributed ML architectures

Other signals

  • develop and deploy machine learning solutions
  • player behavior prediction
  • game performance optimization
  • content recommendation
  • anti-cheat systems