Job description

At Riot, we are investing in League of Legends to grow the game for generations to come. As player needs become more varied and our experiences become more dynamic, machine learning is an increasingly important part of how we help players discover the right experiences, make better decisions in and around the game, and find fair, compelling matches. As a Staff Machine Learning Engineer in League of Legends, you will build applied machine learning systems that directly improve player experience. You will work across player and product problems through data, modeling, experimentation, launch, and iteration, partnering closely with League product, design, Insights, game engineering, and service engineering teams. Your work could span across personalized player experiences, in-game systems, and matchmaking. You will report to the Data Engineering Director in League of Legends and operate as a deeply embedded technical partner to the game team. You will also help strengthen craft standards, knowledge sharing, and technical quality across Riot’s growing ML engineering discipline. Responsibilities: Own end-to-end ML solutions for player-facing problems across personalization, game systems, and matchmaking, from problem framing through production launch and ongoing iteration. Set technical direction for a League ML domain and create reusable modeling, evaluation, and operating patterns that raise the bar beyond your immediate team. Build models, recommenders, ranking systems, and decision logic that help players discover the right champions, builds, modes, content, and return paths based on their needs and context. Develop ML approaches that improve in-game systems and matchmaking quality, balancing player experience, fairness, reliability, and operational constraints. Partner closely with product managers, designers, analysts, and engineers to shape ambiguous opportunities into clear technical plans and shipped player-facing features. Translate gameplay, behavioral, and product telemetry into reliable signals and evaluation frameworks. Design and run experiments to evaluate model quality, player impact, and system tradeoffs. Work directly with game and service engineers to integrate models into League systems and services, including helping define instrumentation and telemetry when needed. Operate independently across multiple partner groups, driving multi-month work with limited day-to-day oversight. Contribute to the ML engineering community at Riot through peer reviews, documentation, craft standards, and shared learnings. Help establish robust monitoring, observability, and support practices for live ML systems as they scale. Required Qualifications: Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience. 6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles. Evidence that your modeling choices have been adopted beyond your immediate team — whether through reusable patterns, shared architectures, or influence on how others approach problems. History of working with complex or unconventional data sources where off-the-shelf feature engineering doesn’t apply. Experience in production environments with interacting models, feedback loops, or systems where model behavior has downstream consequences beyond a single prediction. Comfort with ambiguity — you’ve shipped in situations where the success metric, the right approach, or both were unclear at the start. Track record mentoring engineers across roles and levels; evidence of raising the bar for people around you. Excellent written and verbal communication. Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments is a plus. Experience bridging research and production — translating papers or prototypes into reliable shipped systems — is a plus. Familiarity with ML platform components (model serving, feature stores, ML observability) is a plus. Passion for player experience, games, or creative technology. Desired Qualifications: Experience building ML systems for games, live service products, consumer personalization, or other player-facing digital products. Experience with matchmaking, recommendations, multi-objective optimization, or other systems that must balance competing goals. Familiarity with causal inference, uplift modeling, contextual bandits, reinforcement learning, or other approaches useful for adaptive player experiences. Experience integrating models into latency-sensitive or high-reliability production environments. Experience defining telemetry or instrumentation needs in close partnership with software engineers. Familiarity with responsible AI practices, including fairness, safety, transparency, and operational trustworthiness. Comfort collaborating across a central craft organization and an embedded product team model. For this role, you’ll find success through: Strong applied ML craft Independent execution in ambiguous spaces Thoughtful collaboration with product, design, engineering, and Insights partners Decision-making that prioritizes player value and long-term system health For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role! Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Requirements

Must-haves

Own end-to-end ML solutions for player-facing problems across personalization, game systems, and matchmaking, from problem framing through production launch and ongoing iteration.
Set technical direction for a League ML domain and create reusable modeling, evaluation, and operating patterns that raise the bar beyond your immediate team.
Build models, recommenders, ranking systems, and decision logic that help players discover the right champions, builds, modes, content, and return paths based on their needs and context.
Develop ML approaches that improve in-game systems and matchmaking quality, balancing player experience, fairness, reliability, and operational constraints.
Partner closely with product managers, designers, analysts, and engineers to shape ambiguous opportunities into clear technical plans and shipped player-facing features.
Translate gameplay, behavioral, and product telemetry into reliable signals and evaluation frameworks.
Design and run experiments to evaluate model quality, player impact, and system tradeoffs.
Work directly with game and service engineers to integrate models into League systems and services, including helping define instrumentation and telemetry when needed.
Operate independently across multiple partner groups, driving multi-month work with limited day-to-day oversight.
Contribute to the ML engineering community at Riot through peer reviews, documentation, craft standards, and shared learnings.
Help establish robust monitoring, observability, and support practices for live ML systems as they scale.
Bachelor’s degree or higher in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
6+ years of experience delivering ML systems in production, including 3+ years in applied modeling or ML research roles.
Evidence that your modeling choices have been adopted beyond your immediate team — whether through reusable patterns, shared architectures, or influence on how others approach problems.

Nice-to-haves

Background in reinforcement learning, imitation learning, generative models, or simulation-based training in interactive environments is a plus.
Experience bridging research and production — translating papers or prototypes into reliable shipped systems — is a plus.
Familiarity with ML platform components (model serving, feature stores, ML observability) is a plus.
Experience building ML systems for games, live service products, consumer personalization, or other player-facing digital products.
Experience with matchmaking, recommendations, multi-objective optimization, or other systems that must balance competing goals.
Familiarity with causal inference, uplift modeling, contextual bandits, reinforcement learning, or other approaches useful for adaptive player experiences.
Experience integrating models into latency-sensitive or high-reliability production environments.
Experience defining telemetry or instrumentation needs in close partnership with software engineers.
Familiarity with responsible AI practices, including fairness, safety, transparency, and operational trustworthiness.
Comfort collaborating across a central craft organization and an embedded product team model.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlai

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

401k

Paid Time Off

Parental Leave