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><strong>What You’ll Work On</strong></h3>
<p><strong>Tech: Python, PyTorch, scikit-learn, MLflow/W&amp;B, DataFrames, SQL, AWS (SageMaker/Bedrock)<br></strong></p>
<ul>
<li>Design, train, evaluate, and deploy ML models that power real-time pricing decisions across billions of ad auction events</li>
<li>Build and maintain production ML pipelines end-to-end: feature engineering, training, validation, deployment, monitoring, and retraining</li>
<li>Develop and operate MLOps infrastructure (experiment tracking, model registry, A/B testing frameworks, automated retraining) on AWS using infrastructure-as-code</li>
<li>Integrate LLM and agentic AI components into product workflows, including prompt engineering, orchestration, evaluation, and feedback loops</li>
<li>Build feature pipelines and serving layers that operate at low latency and high throughput, working closely with data and backend engineers</li>
<li>Own model observability and reliability: drift detection, performance monitoring, alerting, SLAs, and post-incident reviews</li>
<li>Contribute to backend services and data pipelines where needed to close the gap between model development and production delivery</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 ML engineering fundamentals: model development, feature engineering, evaluation methodology, and a solid understanding of when (and when not) to apply ML</li>
<li>Hands-on experience deploying and operating ML models in production, including managing model lifecycle, versioning, A/B testing, and monitoring for drift and degradation</li>
<li>Experience building MLOps tooling and infrastructure (e.g., MLflow, W&amp;B, SageMaker Pipelines, Kubeflow, or similar) to support reproducible, automated workflows</li>
<li>Solid Python skills with comfort across the ML ecosystem (PyTorch/TensorFlow, scikit-learn, pandas, Spark) and the ability to write production-quality code — not just notebooks</li>
<li>Experience integrating LLM/agentic AI components into production systems, including evaluation, grounding, and feedback capture</li>
<li>Working knowledge of backend engineering: APIs, async processing, and containerised deployments — enough to ship models as reliable services</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>