TL;DR

Machine Learning Lead (AI Engineering): Leading a team to build scalable, production-ready machine learning systems focused on real-time inventory and personalization with an accent on experimentation frameworks, business impact, and data automation. Focus on designing ML models that improve ARPU, retention, and monetization while driving continuous process improvements and cross-functional collaboration.

What you will do

Translate complex ML insights into clear, actionable business outcomes impacting ARPU, retention, and monetization.
Lead a team of ML engineers to build scalable, production-ready systems.
Design, deploy, and iterate on machine learning models powering real-time inventory and personalization.
Collaborate cross-functionally with engineering, product, and growth teams to align experimentation with business goals.
Make strategic trade-offs between scalability, system quality, and time-to-impact.
Drive continuous process improvements to enhance data automation, speed of iteration, and experimentation culture.

Requirements

4+ years of experience in applied machine learning or data systems with product delivery focus.
2+ years experience in adtech, gaming, or personalization domains preferred.
Leadership experience managing teams of 3+ engineers in agile environments.
Deep knowledge of ML systems design, experimentation frameworks, metrics, and real-time data pipelines.
Strong business fluency in monetization, retention, ARPU, and growth loops.
Hybrid mindset: hands-on technical architect and product strategist.
Excellent communication skills to translate technical complexity into business impact.
Dealbreaker: No pure research or infrastructure profiles without product-market fit exposure.

Nice to have

Experience in multi-objective optimization balancing multiple KPIs.
Background in startups or growth-stage environments with rapid iteration cycles.
Understanding of revenue prediction, personalization, and reinforcement learning loops.

Culture & Benefits

Employee Stock Options program.
Opportunities to scale a unique product.
Various bonus systems including performance-based, referral, additional paid leave, and personal learning budget.
Paid volunteering opportunities.
Work location of your choice: office, remote, opportunity to work and travel.
Personal and professional growth supported by feedback and promotion processes.