Join our team<br>

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><div>Don’t see a role that fits? Apply anyway!</div>
<div>&nbsp;</div>
<div>We’re always looking for exceptional people who want to build the future, and some of our best hires started with a conversation before a job description existed.</div>
<div>&nbsp;</div>
<div>Tell us what you’re great at, what you want to work on, and how you’d add value at pubX.</div>
<div>If there’s alignment - now or soon - we’ll be in touch.</div></body></html>

Future pubXer (Open Application)

<html><head></head><body><p><strong>NOTE: This role is a Hybrid role, it requires in person meetups in Bengaluru, India 🇮🇳<br></strong></p>
<h3>Why This Role Exists</h3>
<div>PubX builds next-generation publisher-first agentic advertising infrastructure. Our AI makes real-time, revenue-critical 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.</div>
<h3>The problem we’re solving</h3>
<div>Digital publishers leave significant revenue on the table because their tools are are still largely manual, static or simple rule-based. Every ad impression is unique, but most advertising systems treat them the same. PubX's AI analyzes bid-stream data and historical patterns to arrange optimal deals, in near real-time.</div>
<p>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<br></strong></h3>
<p><strong>Tech: Python and/or TypeScript, FastAPI/Node, Kafka/SQS, AWS, Terraform/CDK, AI stack</strong></p>
<ul>
<li>Design, build, and maintain backend services and APIs (REST/GraphQL/gRPC) that power agentic AI features and core product workflows i.e. MCP and A2A protocols,</li>
<li>Develop web applications that integrate tightly with ML/LLM-driven systems (streaming UX, feedback capture, citations/grounding, stateful sessions),</li>
<li>Build and operate event-driven components using Kafka and message queues (e.g., SQS/RabbitMQ), design asynchronous workflows for long-running agent jobs,</li>
<li>Deploy and operate services on AWS (e.g., ECS/EKS/Lambda, API Gateway/ALB, RDS/DynamoDB, S3), using infrastructure-as-code and secure-by-default patterns,</li>
<li>Improving our reliability, observability, and developer workflows over time, structured logs, metrics, tracing (incl. agent/tool traces), dashboards, alerting, and post-incident reviews.</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 backend engineering fundamentals: API-first design, data modeling, performance, and security considerations.</li>
<li>Comfortable integrating LLM/ML components into product systems (streaming, async jobs, state, caching, evaluation).</li>
<li>Strong hands-on experience with AI-assisted development workflows in a real codebase (not just demos), including using agents to navigate large repos, apply multi-file changes, and iterate via tests.</li>
<li>Experience building distributed systems with event-driven patterns (Kafka/queues).</li>
<li>Hands-on AWS experience with production operations, monitoring, incident response, and cost-awareness.</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>

Senior Full-Stack AI Engineer (Agentic AI)

<html><head></head><body><h3>Why This Role Exists</h3>
<div>PubX builds next-generation agentic advertising infrastructure. Our AI makes real-time, revenue-critical decisions for digital publishers and advertisers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We've priced over 1 trillion programmatic auctions, we’re currently ranked #5 globally in Prebid Analytics Adapter Rankings, and growing.</div>
<h3>The problem we’re solving</h3>
<div>Digital publishers and advertisers leave significant revenue on the table because ad sales are still largely manual, static, or rule-based. The reality is every camp is unique, but most ad campaign management systems lack the intelligence to evaluate context, demand, and deal potential in real time.</div>
<div>&nbsp;</div>
<div>As a founding member of AgenticAdvertising.org, we're building the next generation of autonomous advertising infrastructure.</div>
<h4>Who You Are</h4>
<ul>
<li>A pragmatic engineering mindset: you know when to use an agent framework, when a simpler workflow is better, and how to balance model capability, reliability, latency, cost, and user experience.</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>
<h4>What You’ll Work On</h4>
<ul>
<li>Design, build, and maintain backend services and APIs across REST, GraphQL, and gRPC that power agentic AI features and core product workflows.</li>
<li>Build production-grade AI agent systems using modern frameworks and patterns such as LangChain, LangGraph, tool/function calling, structured outputs, multi-step planning, stateful agent sessions, human-in-the-loop workflows, and agent memory.</li>
<li>Design and integrate agent interoperability patterns, including Model Context Protocol (MCP) for tool and data access, and Agent-to-Agent (A2A) communication patterns for multi-agent collaboration.</li>
<li>Develop web applications that integrate tightly with ML and LLM-driven systems, including, stateful conversations, retries, fallbacks, and explainability surfaces.</li>
<li>Build and operate event-driven components using Kafka and message queues such as SQS or RabbitMQ, designing asynchronous workflows for long-running agent jobs, background reasoning tasks, tool execution, and orchestration pipelines.</li>
<li>Implement observability, evaluation, and debugging workflows for AI systems using tools such as Langfuse, LangSmith, OpenTelemetry, structured logs, metrics, tracing, agent/tool traces, dashboards, alerting, and post-incident reviews.</li>
<li>Deploy and operate services on AWS, including ECS, EKS, Lambda, API Gateway, ALB, RDS, DynamoDB, S3, and related services, using infrastructure-as-code and secure-by-default engineering patterns.</li>
<li>Continuously improve reliability, performance, cost efficiency, developer experience, and production-readiness across both traditional backend systems and AI-native workflows.</li>
</ul>
<h4>What We’re Looking For</h4>
<div>We’re looking for an experienced engineer who has built production systems and enjoys solving practical problems with AI. You should be comfortable working across backend engineering, cloud infrastructure, and LLM-powered product experiences.</div>
<div>&nbsp;</div>
<h4>You’ve likely worked with:</h4>
<ul>
<li>Strong backend engineering fundamentals, including API-first design, data modeling, distributed systems, performance, security, authentication, authorization, and production operations.</li>
<li>Hands-on experience integrating LLMs or ML components into real product systems, including streaming responses, async jobs, state management, caching, retrieval, grounding, evaluations, and monitoring.</li>
<li>Practical experience with agentic AI frameworks and patterns, such as LangChain, LangGraph, LlamaIndex, OpenAI/Anthropic APIs, tool calling, structured outputs, agent routing, multi-agent workflows, and human-in-the-loop systems.</li>
<li>Experience with LLM observability and evaluation workflows, ideally using tools such as Langfuse, LangSmith, OpenTelemetry, custom evaluation harnesses, prompt/version management, datasets, regression testing, and LLM-as-judge approaches.</li>
<li>Strong hands-on experience with AI-assisted development workflows in a real codebase, not just demos, including using coding agents to navigate large repositories, propose multi-file changes, write and update tests, review diffs, and iterate safely.</li>
<li>Experience working with distributed systems with event-driven patterns, including Kafka, SQS, RabbitMQ, pub/sub architectures, background workers, retries, dead-letter queues, idempotency, and workflow orchestration.</li>
<li>Hands-on AWS experience with production operations, monitoring, incident response, infrastructure-as-code, cost awareness, and secure deployment practices.&nbsp;</li>
</ul>
<h4>Nice to Have</h4>
<ul>
<li>Experience with MCP servers or clients, A2A-style agent communication, tool registries, agent authentication, permissions, audit logging, and secure tool execution.</li>
<li>Experience with retrieval-augmented generation systems, vector databases, embedding pipelines, reranking, hybrid search, citation generation, and grounding quality evaluation.</li>
<li>Experience designing safeguards for LLM systems, including prompt-injection mitigation, output validation, PII handling, policy enforcement, sandboxing, rate limiting, and abuse monitoring.</li>
<li>Experience with modern AI developer tooling such as Cursor, Claude Code, GitHub Copilot, OpenAI Codex, or similar coding-agent workflows in production engineering teams.</li>
</ul>
<h4>Bonus (not required):</h4>
<ul>
<li>Experience with AdTech or other high volume real-time systems<br>&nbsp;</li>
</ul>
<h4>Who This Role Will Suit</h4>
<ul>
<li>This role suits engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving.</li>
<li>We’re a distributed team with a growing engineering presence in India, so comfort with async collaboration and clear written communication is important.</li>
<li>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.</li>
</ul>
<h4>Company Benefits</h4>
<ul>
<li>Competitive 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&nbsp;</li>
</ul>
<h4>Interview Process</h4>
<ul>
<li>Our process is designed to be practical and respectful.</li>
<li>CV &amp; Profile Review – Relevant experience and background</li>
<li>Initial Chat (30 mins) – Motivation and role fit</li>
<li>Architecture Interview (60 mins) – Architecture, design choices, and real scenarios</li>
<li>Agentic Coding Exercise (60 mins) – A small task related to the role, executed by agents<br>&nbsp;</li>
</ul>
<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>&nbsp;</div>
<div>We will process your personal data in accordance with our Recruitment Privacy Notice: https://pubx.ai/privacy/recruitment/</div></body></html>

Senior AI Engineer (Agentic AI)

<html><head></head><body><h4><span style="font-size: 14px;">Why This Role Exists</span></h4>
<p><span style="font-size: 14px;">PubX builds next-generation agentic advertising infrastructure. Our AI makes real-time, revenue-critical decisions for digital publishers and advertisers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We've priced over 1 trillion programmatic auctions, we’re currently ranked #5 globally in Prebid Analytics Adapter Rankings, and growing.<br></span></p>
<h4><span style="font-size: 14px;">The problem we’re solving<br></span></h4>
<p><span style="font-size: 14px;">Digital publishers and advertisers leave significant revenue on the table because ad sales are still largely manual, static, or rule-based. The reality is every camp is unique, but most ad campaign management systems lack the intelligence to evaluate context, demand, and deal potential in real time.<br>&nbsp;<br>As a founding member of AgenticAdvertising.org, we're building the next generation of autonomous advertising infrastructure.<br></span></p>
<h4><span style="font-size: 14px;">Who You Are<br></span></h4>
<ul>
<li><span style="font-size: 14px;">A pragmatic engineering mindset: you know when to use an agent framework, when a simpler workflow is better, and how to balance model capability, reliability, latency, cost, and user experience.</span></li>
<li><span style="font-size: 14px;">Communicate technical ideas well in writing and conversation to both technical and non-technical audiences.</span></li>
<li><span style="font-size: 14px;">Write clean, well-tested code with thoughtful abstractions that’s easy to extend and operate.</span></li>
<li><span style="font-size: 14px;">Learn quickly when things are unfamiliar by prototyping, then hardening and documenting what you ship.<br></span></li>
</ul>
<h3><span style="font-size: 14px;">What You'll Work On</span></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>Who This Role Will Suit</strong></div>
<ul>
<li>This role suits engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving.</li>
<li>We’re a distributed team with a growing engineering presence in India, so comfort with async collaboration and clear written communication is important.</li>
<li>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.</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>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>

Senior Data Engineer (Agentic AI)

<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>

Job Openings

Future pubXer (Open Application)

Senior Full-Stack AI Engineer (Agentic AI)

Senior AI Engineer (Agentic AI)

Senior Data Engineer (Agentic AI)

Senior ML Engineer (Agentic AI)