Research Scientist II, Game AI - Tech Research

Riot Games, Inc.about 1 month ago
Los Angeles, CA, United States
Hybrid
Full-time
Junior Level (1-3 years)

Job Description

Job Description

Tech Research performs applied research to accelerate the pace of building AI enabled applications for our game teams and to establish AI as a core capability at Riot.

As a Research Scientist II, you will work with an R&D game team to develop AI agents to play the game like a human at a high skill level. You will work on the end-to-end development of the agent as well as any infrastructure required to train the agent or integrate the agent into features required by the game teams.

Your Responsibilities

  • Propose, implement, and evaluate algorithms that can play games like a human at a high skill level
  • Work with game teams to propose, define, and develop AI enabled features (e.g. practice opponents, metagame analysis, player coaching)
  • Define and gather game state from the game server needed for training
  • Optimize a distributed deep learning training infrastructure including algorithms, hyperparameters, CPU, memory, and I/O

Required Qualifications

  • MS: in Computer Science or equivalent with 4+ years relevant experience or BS: in Computer Science or equivalent with 6+ years relevant experience
  • Experience: 2+ years experience in reinforcement learning
  • Python
  • PyTorch or TensorFlow
  • Familiarity with GCP or AWS
  • Ability to read research papers and implement similar ideas

Desired Qualifications

  • Knowledge of fundamentals of game theory
  • Game development / game engine (Unreal) experience
  • Distributed computing systems design

Our Perks

Riot has a focus on work/life balance, shown by our open paid time off policy, in addition to 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.

Required Skills

PyTorch
Reinforcement Learning
Distributed Computing Systems
Game Development
GCP
Python
TensorFlow
Game Theory
AWS