Machine Learning Engineering Manager

Reposted Yesterday
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Prague, CZE
Hybrid
Senior level
Gaming • Machine Learning • Software • Esports
The Role
Lead two ML teams across esports verticals, owning end-to-end ML lifecycle, roadmap, delivery, and MLOps. Act as technical bridge to product and infra, unblock teams, drive hiring and performance, introduce AI tooling, and ensure scalable, validated production deployments while managing cross-team dependencies and agile execution.
Summary Generated by Built In

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 ResponsibilitiesLeadership

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 NeedIndividual 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.

Bonus: Industry Experience

Experience in betting, quantitative finance, trading, or sports analytics is a significant advantage.

Haven't we caught you yet?

Help us maintain our position as the No. 1 esports analytics company.

Imagine a culture that embraces change and quickly adapts to global opportunities.

Experience immense potential for personal growth while contributing to an innovative team.

Work remotely or in a hybrid style – whatever suits you best.

Enjoy our brand new Norse-myth-inspired offices in Prague.

Improve your English with company-sponsored language courses.

Participate in quarterly team-building activities to bond with your team.

Benefit from a sponsored Multisport card.

Skills Required

  • 4+ years as a hands-on ML engineer
  • 3+ years managing a team of 5-10 people focused on ML or mathematical models
  • Practical experience with MLOps (production deployment and infrastructure best practices)
  • Understanding of model development and validation challenges and best practices
  • Concrete experience using AI tools to accelerate ML workflows (code generation, prototyping, validation)
  • Strong communication and collaboration skills
  • Ability to provide constructive feedback, support growth, and set clear goals
  • Strong organizational skills for tracking progress across multiple teams and managing blockers
  • Experience in betting, quantitative finance, trading, or sports analytics
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The Company
159 Employees
Year Founded: 2018

What We Do

Oddin.gg is a global B2B provider of end-to-end esports betting solutions, specializing in esports data science. The company provides betting operators with real-time odds feeds, automated risk management, and embeddable iFrame integrations. By leveraging machine learning and mathematical models, Oddin.gg optimizes partner profitability and user engagement across a wide range of esports titles, including Dota2 and League of Legends.

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