We are a fast-growing technology company building a next-generation monetization intelligence layer for mobile applications.

Our platform integrates with leading monetization and mediation ecosystems and treats monetization as a system-level engineering challenge. We orchestrate demand sources, user cohorts, and ad experience optimization in real time to help mobile publishers maximize long-term user value.

We operate at high scale, with strict latency requirements and a strong emphasis on clean architecture, experimentation, and data-driven decision making.

The Challenge

As a Backend Engineer, you will own the architecture and implementation of high-throughput, low-latency services that power our core decision engine.

This is not a CRUD-based backend role. You will design and build a real-time orchestration layer that intercepts requests and performs dynamic routing and configuration decisions within milliseconds, under strict performance constraints.

What You Will Do

Architect Core Systems
Design and implement scalable services using Kotlin (JVM) deployed on AWS EKS, handling highly concurrent traffic with tight latency budgets.

End-to-End Ownership
Own the full development lifecycle: from refining requirements with stakeholders to design, implementation, testing, deployment, and production monitoring.

Build Monetization Intelligence Logic
Develop advanced optimization features such as yield optimization, user engagement modeling, experimentation frameworks, and quality controls.

Enforce Engineering Standards
Promote and apply Domain-Driven Design (DDD) and Clean Architecture principles to ensure the domain remains modular, testable, and decoupled from infrastructure.

Technical Leadership & Mentorship
Act as a technical reference within the team. Write technical specifications, conduct code reviews, and drive architectural decisions.

Tech Stack

Language: Kotlin (JVM)
Infrastructure: AWS (EKS, EC2, RDS, DynamoDB, ElastiCache)
Orchestration: Kubernetes (EKS)
Observability: Prometheus, Grafana, Sentry
CI/CD: Git-based CI pipelines
AI-Augmented Engineering: Internal and external LLM-based tools

Must-Have Skills

Backend Engineering Mastery
Strong backend experience. While the stack is Kotlin, we welcome experienced backend engineers from other JVM or systems languages who are open to transitioning.

Architecture & Software Craftsmanship
Deep knowledge of Clean Code principles, Domain-Driven Design (DDD), and Clean/Hexagonal Architecture. Ability to model complex business domains and maintain separation of concerns.

Cloud-Native Engineering
Solid understanding of AWS or equivalent cloud platforms. Strong judgment around database selection (document vs relational), scalability trade-offs, and system performance design.

High-Scale Distributed Systems
Experience designing and operating high-throughput, low-latency services in production environments.

First-Principles Thinking
Comfortable working in ambiguous environments. Strong root cause analysis skills and a deep understanding of distributed system behavior.

Analytical Mindset
Data-driven engineer. Comfortable analyzing logs, metrics, traces, and databases to inform decisions.

Startup Mentality
Thrives in high-velocity environments where pragmatism and execution speed matter.

AI-Augmented Engineering
Uses LLMs and AI tools effectively as productivity multipliers while maintaining strong engineering judgment and accountability for output quality.

Nice-to-Have

• Experience in AdTech, programmatic ecosystems, or monetization systems

• Production experience with Kubernetes

• Exposure to large-scale data processing (Spark, Databricks, or similar)

• Infrastructure as Code (Terraform or equivalent)

• Experience with OLAP databases for metrics aggregation