AI / Machine Learning Engineer – Real-Time Bidding (RTB)

We are building a large-scale Real-Time Bidding (RTB) platform processing tens of billions of requests per day, running on horizontally scalable Kubernetes infrastructure.

As RTB enters the AI-driven era, we are looking for an AI / Machine Learning Engineer who wants to work on real-world, production-grade AI systems — not isolated models, but AI integrated directly into live decision-making systems.

This role focuses on applying AI to:

• traffic throttling and optimization
• configuration and rule discovery
• anomaly detection and alerting
• automated and semi-automated decision systems

You will work at the intersection of RTB logic, large-scale data, and applied AI.

• Design and build AI-driven decision support systems for RTB
• Apply AI to:
• Traffic throttling and weight optimization
• Configuration and rule recommendations
• Anomaly detection (performance, revenue, fraud-like behavior)
• Smart alerting with actionable outputs
• Analyze large-scale, time-series RTB data
• Build feedback loops: outcome → learning → action
• Combine rule-based logic with ML models (hybrid systems)
• Work closely with backend engineers to bring models into production
• Improve system efficiency, margin, and traffic quality using AI

• Experience with machine learning or applied AI in production or near-production systems
• Strong understanding of data-driven decision-making
• Experience working with large datasets and analytics workflows
• Knowledge of:
• Anomaly detection
• Time-series analysis
• Statistical or ML-based pattern detection
• Ability to translate model outputs into practical system actions
• Solid engineering mindset (code quality, performance awareness)

• RTB / AdTech high-scale backend systems
• ClickHouse for large-scale analytics
• PostgreSQL for configuration and control data
• Kubernetes and distributed systems
• Golang-based backend services

(You don’t need to master everything on day one — willingness to learn matters.)

• Curiosity about how real-time systems behave under load
• Interest in turning AI insights into actual production impact
• Pragmatic mindset — not “AI magic”, but measurable results
• Willingness to experiment, validate, and iterate
• Comfort working in complex, high-traffic environments

• Any exposure to RTB, AdTech, SSP/DSP systems
• Experience with reinforcement learning, bandits, or optimization problems
• Familiarity with monitoring, alerting, or fraud detection
• Experience deploying ML models into live systems
• Background in backend engineering or data engineering

• Work on real, high-scale RTB systems
• Apply AI where every decision has immediate impact
• Opportunity to shape how AI is used in a production RTB platform
• Close collaboration with experienced RTB and backend engineers
• A technical environment where models don’t live in notebooks — they drive decisions