Work closely with developers, designers, and data scientists to identify and define improvement opportunities
Refine modeling and feature strategy using techniques such as reinforcement learning
Establish pipeline architecture patterns in partnership with central teams; build reusable training and evaluation pipelines that can be adapted to Riot-wide use cases
Drive the application of ML/RL systems to improve decision-making and adaptation
Establish research-to-production practices; balance cutting-edge techniques with production reliability

Key requirements

Work closely with developers, designers, and data scientists to identify and define improvement opportunities
Refine modeling and feature strategy using techniques such as reinforcement learning
Establish pipeline architecture patterns in partnership with central teams; build reusable training and evaluation pipelines that can be adapted to Riot-wide use cases
Drive the application of ML/RL systems to improve decision-making and adaptation
Establish research-to-production practices; balance cutting-edge techniques with production reliability

Jobs at Riot Games

3 active roles from this employer in the JobMatcher catalog.

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Jobs at Riot Games

3 active roles from this employer in the JobMatcher catalog.

All company jobs

About the role

Machine Learning Engineer III – on-site in Los Angeles, United States – Riot Games. As a Machine Learning Engineer III on an unpublished R&D game project at Riot Games, you will own the strategy, design, and implementation of ML capabilities. You will work closely with developers, designers, and data scientists to understand their needs, and you will be hands-on in conceptualizing, planning, implementing, and maintaining ML-driven solutions. We’re looking for someone who is passionate about enabling great player experiences, thrives in cross-disciplinary collaboration, stays current on the latest ML techniques, and can apply them pragmatically to challenging product problems. You will report to the Engineering Manager within the R&D game project.

Responsibilities

Work closely with developers, designers, and data scientists to identify and define improvement opportunities
Refine modeling and feature strategy using techniques such as reinforcement learning
Establish pipeline architecture patterns in partnership with central teams; build reusable training and evaluation pipelines that can be adapted to Riot-wide use cases
Drive the application of ML/RL systems to improve decision-making and adaptation
Establish research-to-production practices; balance cutting-edge techniques with production reliability
Establish evaluation practices in partnership with data scientists and analytics partners; guide experiment design for measuring model impact and set technical evaluation standards
Define reliability standards in partnership with design and engineering
Advance

About the role

Machine Learning Engineer III – on-site in Los Angeles, United States – Riot Games. As a Machine Learning Engineer III on an unpublished R&D game project at Riot Games, you will own the strategy, design, and implementation of ML capabilities. You will work closely with developers, designers, and data scientists to understand their needs, and you will be hands-on in conceptualizing, planning, implementing, and maintaining ML-driven solutions. We’re looking for someone who is passionate about enabling great player experiences, thrives in cross-disciplinary collaboration, stays current on the latest ML techniques, and can apply them pragmatically to challenging product problems. You will report to the Engineering Manager within the R&D game project.

Responsibilities

Work closely with developers, designers, and data scientists to identify and define improvement opportunities
Refine modeling and feature strategy using techniques such as reinforcement learning
Establish pipeline architecture patterns in partnership with central teams; build reusable training and evaluation pipelines that can be adapted to Riot-wide use cases
Drive the application of ML/RL systems to improve decision-making and adaptation
Establish research-to-production practices; balance cutting-edge techniques with production reliability
Establish evaluation practices in partnership with data scientists and analytics partners; guide experiment design for measuring model impact and set technical evaluation standards
Define reliability standards in partnership with design and engineering
Advance

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