Your teams own the algorithmic heartbeat of major esports verticals, spanning iconic MOBA titles (Dota 2, LoL) and FPS giants (CS2, Valorant). They manage the entire ML lifecycle—from raw data ingestion and feature engineering to model architecture, rigorous validation, and high-throughput production deployment. While you won't be writing the code yourself, you will serve as the technical anchor who deeply understands this end-to-end flow, ensuring our systems are robust, scalable, and cutting-edge.
A critical pillar of your role is high-level strategic alignment. You will act as the key technical bridge between your teams and product managers, infrastructure champions, and domain game specialists, transforming complex technical capabilities into sharp business outcomes and seamlessly integrating domain insights into our modeling strategy.
Your Responsibilities
Leadership
Lead two teams of 4-6 ML engineers and mathematicians, each focused on a different esports vertical (MOBA, FPS).
Define team staffing needs and drive hiring across both teams.
Manage performance of direct reports. Foster a culture of ownership, accountability, and collaboration.
Set the standard for how the teams work with AI tools - lead by example, encourage experimentation, measure and evaluate AI tools and set up impact on delivery.
Technical Strategy & Roadmap
Partner with senior team members to shape the roadmap; together with a PM ensure alignment with business goals.
Review and challenge key high-level technical decisions - model development and validation, ML architecture etc.
Work on unblocking the teams - support ML Ops and infrastructure best practices.
Evaluate and introduce AI tooling that accelerates model development, validation, and deployment.
Delivery & Execution
Own the agile delivery process; find a good trade-off between delivery and R&D.
Help others break down complex ideas into actionable tasks and research initiatives; drive execution and delivery. Leverage AI tools to accelerate prototyping and iteration cycles.
Manage dependencies across teams and represent the teams externally when resolving cross-team initiatives.
Skills You Need
Individual Contributor Background (4+ years)
4+ years as a hands-on ML engineer.
Understanding of model development and validation challenges, pitfalls, and best practices.
Practical experience with MLOps.
Concrete experience using AI tools to accelerate ML workflows - code generation, prototyping, validation.
Manager (3+ years)
3+ years leading a team of 5–10 people focused on ML or mathematical models.
Strong communication and collaboration skills - creating a supportive, productive environment.
Ability to provide constructive feedback, support growth, and set clear goals.
Strong organizational skills - tracking progress across two teams, identifying blockers, ensuring delivery with clear stakeholder insights.