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Senior Software Engineer — AdTech

Оценка ИИ

Zeta Global — публичная компания (NYSE: ZETA) с сильной технологической базой. Работа над сложными задачами в AdTech на таком масштабе дает отличный профессиональный рост, хотя и подразумевает высокую ответственность.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует глубоких знаний в области систем с экстремально высокой нагрузкой (до 1 млн QPS) и оптимизации задержек на уровне p99. Кандидат должен обладать экспертными знаниями в Java/Go и специфике AdTech (OpenRTB, DSP).

Анализ зарплаты

Медиана95 000 £
Рынок80 000 £ – 120 000 £
Оценка ИИ

Предлагаемая позиция Senior/Staff уровня в Лондоне соответствует рыночному диапазону для крупных технологических компаний. AdTech сектор обычно платит чуть выше среднего из-за высокой технической сложности.

Сопроводительное письмо

I am writing to express my strong interest in the Senior Software Engineer position at Zeta Global. With over 5 years of experience in backend development and a deep focus on high-throughput, low-latency distributed systems, I am excited about the opportunity to contribute to your real-time bidding and decisioning systems. My background in optimizing Java/Go services and managing complex AWS infrastructures aligns perfectly with the technical challenges described in the DSP Engineer role.

In my previous roles, I have successfully handled systems processing hundreds of thousands of QPS, focusing on p99 latency optimization and efficient resource management. I am particularly drawn to Zeta's vision of unifying identity and intelligence through the Zeta Marketing Platform. I am confident that my expertise in OpenRTB, real-time ML integration, and performance engineering will allow me to make an immediate impact on your engineering team and help evolve your AdTech stack.

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Присоединяйтесь к Zeta Global, чтобы создавать высоконагруженные системы будущего в сфере AdTech!

Описание вакансии

WHO WE ARE

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

The Role

We’re looking for a Senior / Staff DSP Engineer to build and evolve Zeta’s real-time bidding and decisioning systems. This is a hands-on role focused on designing and operating low-latency, high-throughput services that process bid requests, score opportunities, apply pacing and budget constraints, and produce bids within strict SLAs.

You’ll work closely with platform, data, and ML teams to integrate retrieval and scoring systems into the bidder while keeping the hot path fast, safe, and observable. This role is ideal for engineers who love distributed systems, performance engineering, and building production-grade systems at extreme scale.

What You’ll Do

  • Real-Time Bidding Systems: Design and build core DSP bidder components that handle high-QPS bid request ingestion, filtering, enrichment, decisioning, and bid response generation within strict time budgets.
  • Auction & Eligibility Logic: Implement auction logic, campaign/line-item eligibility, targeting constraints, frequency controls, supply quality checks, and policy enforcement.
  • Low-Latency Engineering: Optimize p99 latency and tail behavior using timeouts, backpressure, concurrency control, efficient serialization, caching strategies, and careful memory/GC management.
  • Pacing & Budgeting: Build and extend pacing and budget management logic (spend controls, throttling, goal-based pacing, safety constraints) and integrate with control-plane services (campaign config, feature flags, budgets).
  • Scoring & ML Integration: Integrate real-time model scoring and feature retrieval systems with production guardrails (shadow mode, A/B tests, canaries, circuit breakers, fallbacks).
  • Reliability & Operations: Define SLOs and observability (metrics, tracing, structured logging), participate in on-call, and drive post-incident improvements.
  • AWS at Scale: Build and operate services on AWS (compute, networking, autoscaling, streaming, data stores) and partner on cost/performance optimization.

Engineering Leadership (Staff scope): For staff-level candidates, drive technical direction for parts of the bidder stack (interfaces, service boundaries, performance budgets) and mentor engineers through design/code reviews and best practices.

Senior: 5+ years of software engineering (with substantial backend/distributed systems work). Staff: 8+ years with demonstrated ownership of large-scale, production systems.

  • Proven experience building low-latency, high-QPS services (real-time decisioning, trading, ad serving, marketplaces, or similar).
  • Strong proficiency in Java and/or Go for performance-oriented backend development.
  • Solid understanding of distributed systems fundamentals: consistency trade-offs, caching, failure modes, retries/timeouts, backpressure, load shedding.
  • Experience operating production services on AWS and making practical cost/performance trade-offs.
  • Experience with data stores and patterns used in real-time systems (e.g., Redis/Memcached, DynamoDB/Cassandra, Kafka/Kinesis).
  • Strong debugging skills across services: profiling, tracing, heap/GC analysis, latency investigations.
  • Clear communicator and collaborator; able to work effectively with engineering, product, and ML partners.
  • Able to explain complex systems to mixed audiences.
  • Hands-on DSP / RTB experience: OpenRTB, exchanges, SSP integrations, seats/accounts, bid request/response handling.
  • Experience with pacing/budgeting systems, bid shading, dynamic floors, and supply quality/brand-safety gating.
  • Experience integrating ML ranking/scoring in real time (feature retrieval, embeddings, model serving, inference optimization).
  • Experience building privacy-aware systems (consent/identity signals, PII-safe logging, governance controls).
  • Experience operating extremely high-scale systems (10^5–10^6+ QPS), multi-region design, or hybrid/colocation architectures.
  • Kubernetes/service mesh familiarity and/or deep Linux networking/performance knowledge (eBPF is a plus).

BENEFITS & PERKS

  • Excellent medical, dental, and vision coverage

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases

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Навыки

  • AWS
  • Kubernetes
  • Microservices
  • Redis
  • Kafka
  • Distributed Systems
  • Java
  • DynamoDB
  • Go
  • Cassandra
  • Low Latency
  • Kinesis
  • OpenRTB

Возможные вопросы на собеседовании

Проверка понимания работы с высоконагруженными системами и минимизации задержек.

Как бы вы подошли к оптимизации p99 задержки в Java-приложении, обрабатывающем 100к+ запросов в секунду?

Оценка опыта работы с протоколами рекламных технологий.

Опишите жизненный цикл OpenRTB запроса от момента получения до формирования ставки.

Проверка навыков проектирования отказоустойчивых систем.

Как реализовать систему контроля бюджета (pacing) в распределенной среде, чтобы избежать перерасхода?

Оценка умения интегрировать ML-модели в реальном времени.

Какие стратегии вы используете для интеграции ML-скоринга в 'hot path' без значительного увеличения задержки?

Проверка навыков работы с облачной инфраструктурой и стоимостью.

Как вы оптимизируете затраты на AWS при работе с высокочастотным трафиком и большими объемами данных в Kinesis/DynamoDB?

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