Join our team<br>

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PubX builds next-generation publisher-first agentic advertising infrastructure. Our AI makes real-time, revenue-critical pricing decisions for digital publishers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We’re currently ranked #5 globally in Prebid Analytics Adapter Rankings, and growing.

Join our team

<html><head></head><body><h3><strong>Why This Role Exists</strong></h3>
<p>PubX builds next-generation publisher-first agentic advertising infrastructure. Our AI makes real-time, revenue-critical pricing decisions for digital publishers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We’re currently ranked #5 globally in Prebid Analytics Adapter Rankings, and growing.</p>
<h3><strong>The problem we’re solving</strong></h3>
<p>Digital publishers leave significant revenue on the table because ad pricing is still largely manual, static or simple rule-based. Every ad impression is unique, but most pricing systems treat them the same. PubX's AI analyzes bid-stream data and historical patterns to arrange optimal deals, in near real-time.<br><br>As a founding member of AgenticAdvertising.org, we're building the next generation of autonomous advertising infrastructure.</p>
<h3>What You'll Work On</h3>
<p><strong>Tech: Python, SQL, Spark, Airflow, dbt, Kafka/Kinesis/SQS, AWS, Terraform/CDK, modern ETL</strong></p>
<ul>
<li>Design and maintain high-volume data pipelines (batch + streaming) powering agentic AI features and core product workflows</li>
<li>Build event-driven components using Kafka and message queues, including idempotency patterns, replay strategies, and backfill mechanisms</li>
<li>Develop data models and transformation layers (lakehouse patterns, dbt-style modeling) supporting both analytics and ML/AI consumption</li>
<li>Own data quality and reliability: schema management, validation, lineage, SLAs, and incident response</li>
<li>Enable AI/ML workflows with robust datasets for training, evaluation, feature generation, and feedback loops from production agents</li>
<li>Deploy and operate data infrastructure on AWS using infrastructure-as-code</li>
</ul>
<h3><strong>What We’re Looking For</strong></h3>
<div>We’re looking for an experienced engineer who has worked on production systems and enjoys solving practical problems with AI.</div>
<div>&nbsp;</div>
<div><strong>You’ve likely have:</strong></div>
<ul>
<li>Strong data engineering fundamentals: data modeling, partitioning, performance tuning, and cost-aware design for high-volume workloads</li>
<li>Experience buildingstreaming and event-driven systems(Kafka/queues), including handling late/out-of-order events, backfills, and real-world data edge cases</li>
<li>Strong SQL + Python skills, and comfort with modern data stack tooling (e.g., Spark, Airflow/Dagster, dbt, warehouse/lakehouse patterns)</li>
<li>Hands-on AWS experience with production operations for data systems: monitoring, incident response, and security considerations (PII, access control, encryption, auditability)</li>
<li>Familiarity integrating data withAI/ML and agentic systems: feature pipelines, evaluation datasets, grounding/citations inputs, and feedback capture from agent outcomes</li>
</ul>
<div><strong>You tend to:</strong></div>
<ul>
<li>Make pragmatic decisions balancing speed, quality, cost, and risk trade-offs.</li>
<li>Communicate technical ideas well in writing and conversation to both technical and non-technical audiences.</li>
<li>Write clean, well-tested code with thoughtful abstractions that’s easy to extend and operate.</li>
<li>Learn quickly when things are unfamiliar by prototyping, then hardening and documenting what you ship.</li>
</ul>
<div>&nbsp;</div>
<div><strong>Bonus (not required):</strong></div>
<ul>
<li>Experience with AdTech or other high volume real-time systems</li>
</ul>
<h3><strong>Who This Role Will Suit</strong></h3>
<div>This role suits engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving.</div>
<div>&nbsp;</div>
<div>We’re a distributed team with a growing engineering presence in India, so comfort with async collaboration and clear written communication is important.</div>
<div>&nbsp;</div>
<div>We use agentic coding tools heavily (e.g. Cursor and Claude Code) to plan, scaffold, refactor, and debug production code, while maintaining strong engineering judgment and ownership of outcomes.</div>
<h3><strong>Company Benefits</strong></h3>
<ul>
<li>What We OfferCompetitive salary with meaningful equity</li>
<li>Fully remote, async-friendly working</li>
<li>Supportive, low-ego engineering culture</li>
<li>Budget for learning and professional development</li>
</ul>
<div>
<h3><strong>Interview Process</strong></h3>
<div>Our process is designed to be practical and respectful.</div>
<ul>
<li>CV &amp; Profile Review – Relevant experience and background</li>
<li>Initial Chat (30 mins) – Motivation and role fit</li>
<li>Technical Interview (60 mins) – Architecture, design choices, and real scenarios</li>
<li>Practical Exercise + Discussion (60 mins) – A small task related to the role</li>
</ul>
</div>
<div>If you’re interested in building and shaping real systems in a growing product company, at the forefront of AdTech innovation, we’d love to hear from you.</div>
<div><br>We will process your personal data in accordance with our Recruitment Privacy Notice: https://pubx.ai/privacy/recruitment/</div></body></html>

<h3><strong>Why This Role Exists</strong></h3>
<p>PubX builds next-generation publisher-first agentic advertising infrastructure. Our AI makes real-time, revenue-critical pricing decisions for digital publishers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We’re currently ranked #5 globally in Prebid Analytics Adapter Rankings, and growing.</p>
<h3><strong>The problem we’re solving</strong></h3>
<p>Digital publishers leave significant revenue on the table because ad pricing is still largely manual, static or simple rule-based. Every ad impression is unique, but most pricing systems treat them the same. PubX's AI analyzes bid-stream data and historical patterns to arrange optimal deals, in near real-time.<br><br>As a founding member of AgenticAdvertising.org, we're building the next generation of autonomous advertising infrastructure.</p>
<h3>What You'll Work On</h3>
<p><strong>Tech: Python, SQL, Spark, Airflow, dbt, Kafka/Kinesis/SQS, AWS, Terraform/CDK, modern ETL</strong></p>
<ul>
<li>Design and maintain high-volume data pipelines (batch + streaming) powering agentic AI features and core product workflows</li>
<li>Build event-driven components using Kafka and message queues, including idempotency patterns, replay strategies, and backfill mechanisms</li>
<li>Develop data models and transformation layers (lakehouse patterns, dbt-style modeling) supporting both analytics and ML/AI consumption</li>
<li>Own data quality and reliability: schema management, validation, lineage, SLAs, and incident response</li>
<li>Enable AI/ML workflows with robust datasets for training, evaluation, feature generation, and feedback loops from production agents</li>
<li>Deploy and operate data infrastructure on AWS using infrastructure-as-code</li>
</ul>
<h3><strong>What We’re Looking For</strong></h3>
<div>We’re looking for an experienced engineer who has worked on production systems and enjoys solving practical problems with AI.</div>
<div> </div>
<div><strong>You’ve likely have:</strong></div>
<ul>
<li>Strong data engineering fundamentals: data modeling, partitioning, performance tuning, and cost-aware design for high-volume workloads</li>
<li>Experience buildingstreaming and event-driven systems(Kafka/queues), including handling late/out-of-order events, backfills, and real-world data edge cases</li>
<li>Strong SQL + Python skills, and comfort with modern data stack tooling (e.g., Spark, Airflow/Dagster, dbt, warehouse/lakehouse patterns)</li>
<li>Hands-on AWS experience with production operations for data systems: monitoring, incident response, and security considerations (PII, access control, encryption, auditability)</li>
<li>Familiarity integrating data withAI/ML and agentic systems: feature pipelines, evaluation datasets, grounding/citations inputs, and feedback capture from agent outcomes</li>
</ul>
<div><strong>You tend to:</strong></div>
<ul>
<li>Make pragmatic decisions balancing speed, quality, cost, and risk trade-offs.</li>
<li>Communicate technical ideas well in writing and conversation to both technical and non-technical audiences.</li>
<li>Write clean, well-tested code with thoughtful abstractions that’s easy to extend and operate.</li>
<li>Learn quickly when things are unfamiliar by prototyping, then hardening and documenting what you ship.</li>
</ul>
<div> </div>
<div><strong>Bonus (not required):</strong></div>
<ul>
<li>Experience with AdTech or other high volume real-time systems</li>
</ul>
<h3><strong>Who This Role Will Suit</strong></h3>
<div>This role suits engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving.</div>
<div> </div>
<div>We’re a distributed team with a growing engineering presence in India, so comfort with async collaboration and clear written communication is important.</div>
<div> </div>
<div>We use agentic coding tools heavily (e.g. Cursor and Claude Code) to plan, scaffold, refactor, and debug production code, while maintaining strong engineering judgment and ownership of outcomes.</div>
<h3><strong>Company Benefits</strong></h3>
<ul>
<li>What We OfferCompetitive salary with meaningful equity</li>
<li>Fully remote, async-friendly working</li>
<li>Supportive, low-ego engineering culture</li>
<li>Budget for learning and professional development</li>
</ul>
<div>
<h3><strong>Interview Process</strong></h3>
<div>Our process is designed to be practical and respectful.</div>
<ul>
<li>CV &amp; Profile Review – Relevant experience and background</li>
<li>Initial Chat (30 mins) – Motivation and role fit</li>
<li>Technical Interview (60 mins) – Architecture, design choices, and real scenarios</li>
<li>Practical Exercise + Discussion (60 mins) – A small task related to the role</li>
</ul>
</div>
<div>If you’re interested in building and shaping real systems in a growing product company, at the forefront of AdTech innovation, we’d love to hear from you.</div>
<div>
<br>We will process your personal data in accordance with our Recruitment Privacy Notice: https://pubx.ai/privacy/recruitment/</div>