Principal Software Engineer - PubMatic AgenticOS (Buy Side Bidding Platform)

PubMatic•Redwood City, CA

3d•Hybrid

About The Position

PubMatic is seeking a Principal Software Engineer to lead the design and development of highly scalable, low-latency buy-side advertising technology systems for its AgenticOS platform. The role focuses on real-time bidding, campaign pacing, and optimization in the DSP and performance advertising domains. The engineer will own critical bidding and optimization components, influence system architecture, mentor engineers, and work closely with product, machine learning, and data teams to improve outcomes across large-scale advertising campaigns. The position includes architecting bidder services that handle millions of queries per second with strict latency and reliability requirements, developing campaign and budget optimization capabilities, and building real-time data pipelines. It also involves productionizing machine learning models, supporting programmatic advertising protocols, improving distributed-system performance, and maintaining data quality, governance, privacy, and compliance. PubMatic operates a hybrid schedule with three days in the office and two days working remotely. PubMatic describes itself as a scaled digital advertising platform serving publishers, media buyers, commerce companies, and data owners.

Requirements

At least eight years of software engineering experience, with significant depth in ad tech, performance advertising, or building buy-side DSP bidders.
Strong hands-on experience with Golang for low-latency, high-scale backend systems.
Proven experience designing and operating real-time bidding platforms, bidders, or ad-serving systems.
Deep understanding of campaign pacing, budget allocation, and performance optimization algorithms.
Experience with streaming and distributed systems, such as Kafka, Flink, or Spark Streaming.
Strong familiarity with OpenRTB, VAST, and programmatic advertising workflows.
Solid knowledge of SQL and PostgreSQL, and large-scale analytics platforms such as Snowflake, BigQuery, or similar.
Experience with cloud platforms such as AWS, GCP, or Azure, and containerized environments such as Docker and Kubernetes.
Strong foundation in data structures, algorithms, and distributed-system design.
Experience working cross-functionally with product, data science, analytics, and machine learning teams.
Ability to own systems end-to-end, influence architecture, and deliver at scale.
Excellent communication skills and ability to drive technical direction and mentor others.
Bachelor’s degree in engineering (CS/IT) or an equivalent degree from a well-known institute or university.

Responsibilities

Architect, design, and build high-throughput, ultra-low-latency real-time bidding systems for buy-side advertising platforms.
Lead development of bidder services handling millions of queries per second with strict latency and reliability SLAs using Golang, C, or C++.
Design and implement campaign pacing, budget management, and delivery optimization algorithms across objectives such as CPA, ROAS, CTR, and CVR.
Own core systems for deal execution, auction participation, bid shading, and supply path optimization.
Build and evolve real-time and near-real-time data pipelines for impressions, clicks, conversions, and attribution using Kafka, Flink, or Spark.
Collaborate with machine learning engineers to productionize bid optimization, contextual bandit, reach forecasting, and incrementality models.
Integrate and maintain support for programmatic advertising protocols, including OpenRTB, VAST, and CTV-specific standards.
Drive architectural decisions concerning scalability, fault tolerance, data consistency, and latency optimization.
Ensure data quality, governance, privacy, and compliance across the performance advertising stack.
Conduct performance profiling, latency tuning, and production debugging of distributed systems.
Mentor engineers, establish coding and design standards, and lead technical reviews across teams.
Evaluate and introduce technologies, auction strategies, and optimization frameworks.