Lead Backend Engineer (Node.js) – AdTech / RTB

Quick Facts

You’ll work on backend systems where milliseconds directly impact business results. Join a small, senior engineering team building a programmatic advertising platform that processes real-time auctions and makes ad-serving decisions in milliseconds, with direct influence on architecture, performance, and technical direction.

Description

Lead the development of high-throughput, low-latency backend services for real-time bidding and programmatic advertising workflows using Node.js and TypeScript. Take ownership of technical and architectural decisions while optimizing latency, throughput, reliability, and resource usage.

Responsibilities

• Build and scale high-throughput, low-latency backend services using Node.js and TypeScript
• Work on real-time bidding flows, integrations, and performance-critical backend components
• Design solutions for systems processing large volumes of concurrent requests
• Optimize latency, throughput, reliability, and resource usage
• Take ownership of technical and architectural decisions
• Work closely with experienced engineers on production systems where performance matters
• Investigate bottlenecks, production issues, and opportunities for further optimization

Requirements

• Strong commercial experience with Node.js and TypeScript
• Experience building high-throughput, low-latency backend systems
• Good understanding of distributed systems, scalability, and performance optimization
• Experience designing and maintaining production-grade APIs and backend services
• Ability to make independent technical decisions and take ownership of solutions
• Fluent English
• Experience with RTB / programmatic advertising
• Knowledge of OpenRTB 2.5 / 2.6
• Experience with IAB standards and ad formats
• Background in DSP, SSP, ad exchanges, or Prebid
• Experience with event-driven architectures, caching, and performance-sensitive systems

Benefits

• 100% remote
• Private medical healthcare and multisport
• Work in a small, senior engineering team
• Real influence on architecture and technical decisions
• Access to modern AI engineering tools with high usage limits
• Practical AI culture: tools support engineering work while technical judgment remains with the engineer
• Product impact where backend performance, scalability, and latency have a direct and measurable outcome