Ad tech platform with ML-driven mediation, exchange, and user acquisition for mobile
Appodeal operates a full-stack ad tech business—SDK mediation, programmatic exchange, and user acquisition—powered by in-house ML models. The tech stack is heavily data-first (Databricks, Kafka, Spark, MLflow, ClickHouse, Druid) and reveals an infrastructure built for real-time ML at scale: data pipeline work, bid strategy modeling, and GPU optimization are all active projects. Hiring momentum is accelerating, with data roles leading the department mix, signaling expansion of the ML backbone and data infrastructure that drives their monetization engine.
Notable leadership hires: Board Director
Appodeal is a global ad tech company serving mobile publishers and developers with three integrated products: an open-source SDK for ad mediation, a programmatic exchange, and user acquisition tools. The company also operates its own publishing division (casual and hybrid-casual games) and runs Appodeal Accelerator, a program offering guidance and capital to mobile developers. Based in McLean, Virginia, with operations spanning the United States and Spain, Appodeal runs production ML models daily to optimize ad placement and monetization, and was ranked among the top five ad SDKs globally in 2025.
Core: Python, PySpark, Pandas, PyTorch, Databricks, Apache Spark, Kafka, ClickHouse, Druid, MLflow. Infrastructure: AWS, Kubernetes, Docker. Data: Delta Lake, Delta Live Tables, Dagster, Airflow. Analytics: Tableau. Also uses Rust, Redis, ScyllaDB, Google Ads, Gemini.
Core projects: ad targeting models, bid strategy optimization, mobile growth platform, ML backbone development, and scaling user acquisition across Google Ads and Meta. Infrastructure focus: data pipeline development, model inference reliability, GPU workload optimization, and strengthening data infrastructure.
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Appodeal, Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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