Machine Learning Engineer III - Unpublished R&D Product

Riot Games•Los Angeles, CA

7d•$229,200 - $319,500

About The Position

As a Staff Machine Learning Engineer on an unpublished R&D game project, you will own the strategy, design, and implementation of ML capabilities.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 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.

Requirements

6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles, ideally within real-time or interactive environments
Experience with reinforcement learning, including applying RL to practical production problems
Experience in production environments with interacting models, feedback loops, or systems where model behavior has downstream consequences beyond a single prediction
Proficiency with modern ML/data science libraries and frameworks such as PyTorch, TensorFlow, pandas, etc.
Experience collaborating regularly with teammates across multiple disciplines
Strong communication skills across technical and non-technical audiences
B.S. in Computer Science or equivalent experience
Proficiency in Python or similar programming languages

Nice To Haves

Experience with multi-agent reinforcement learning
Experience in developer tooling
Experience implementing framework-level UI systems
Experience in infrastructure and MLOps, such as ML cloud services, containers, and model telemetry
Experience working in a AAA game engine (e.g. Unity, Unreal)
Proficiency in C++

Responsibilities

Work closely with developers, designers, and data scientists to identify and define improvement opportunities
Refine modeling and feature strategy for 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 and strengthen engineering best practices, including code quality, automated testing, and maintainable architecture
Mentor engineers and partner disciplines through documentation, reviews, and technical design guidance
Identify technical risks and bottlenecks, proposing pragmatic solutions to keep projects moving forward