Position Overview
As a Staff Data Scientist – VALORANT, Deep Learning at Riot Games, you will blend expertise in data processing, automation, machine learning (ML), artificial intelligence (AI), and experimental design to inform decisions and drive data-powered products. You will work cross‑functionally to develop production‐quality ML models for skill estimation, game understanding, matchmaking, and personalization that directly enhance player experience. Reporting directly to the Data Science Manager within Valorant Studio, your impact will span gameplay telemetry analysis to the integration of deep learning systems into live game environments.
Key Responsibilities
• Lead the modeling strategy for deep learning production quality ML models.
• Develop predictive features and signals from gameplay telemetry, unstructured game data, and simulation outputs, ensuring quality, interpretability, and reliability.
• Design and implement ML systems using methods including reinforcement learning and imitation learning.
• Define evaluation frameworks for game AI that balance generalizable approaches with genre‑specific metrics.
• Mentor senior and staff‑level ML engineers in advanced ML for game AI and architectural decision‑making.
• Collaborate with game and platform engineers, along with UX teams, to integrate models into production systems that enhance player experience and maintain operational reliability.
• Represent the deep learning team and contribute to shared frameworks.
Required Qualifications
• At least 4 years of experience delivering ML systems in production, including reinforcement learning, imitation learning, or simulation‑based training in interactive environments.
• Proven ability to design modeling strategies and architectures.
• Expertise in developing predictive features and signals from gameplay telemetry, simulation data, or other complex interactive environments.
• Strong track record in building deep learning systems for dynamic, player‑facing environments.
• Hands‑on experience with ML methods such as reinforcement learning and imitation learning.
• Mastery of experiment design, model evaluation, and optimization for deep learning.
• Experience incorporating human considerations into AI applications, including responsible AI practices and UX best practices.
• Demonstrated experience mentoring engineers and collaborating with cross‑disciplinary teams.
• Familiarity with integrating deep learning models in live game environments alongside game and platform engineers.
Benefits & Perks
• Emphasis on work/life balance with an open paid time off policy and flexible work schedules.
• Comprehensive medical, dental, and life insurance coverage.
• Generous parental leave for you, your spouse/domestic partner, and children.
• 401(k) plan with company match.
