PubX builds next-generation publisher-first agentic advertising marketplace 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.
The problem we’re solving
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.
As a founding member of AgenticAdvertising.org, we're building the next generation of autonomous advertising infrastructure.
What You’ll Work On
- 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,
- Develop web applications that integrate tightly with ML/LLM-driven systems (streaming UX, feedback capture, citations/grounding, stateful sessions),
- Build and operate event-driven components using Kafka and message queues (e.g., SQS/RabbitMQ), design asynchronous workflows for long-running agent jobs,
- 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,
- Improving our reliability, observability, and developer workflows over time, structured logs, metrics, tracing (incl. agent/tool traces), dashboards, alerting, and post-incident reviews.
What We’re Looking For
We’re looking for an experienced engineer who has worked on production systems and enjoys solving practical problems with AI.
You’ve likely have:
- Strong backend engineering fundamentals: API-first design, data modeling, performance, and security considerations.
- Comfortable integrating LLM/ML components into product systems (streaming, async jobs, state, caching, evaluation).
- 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.
- Experience building distributed systems with event‑driven patterns (Kafka/queues).
- Hands‑on AWS experience with production operations, monitoring, incident response, and cost‑awareness.
You tend to:
- Make pragmatic decisions balancing speed, quality, cost, and risk trade‑offs.
- Communicate technical ideas well in writing and conversation to both technical and non‑technical audiences.
- Write clean, well‑tested code with thoughtful abstractions that’s easy to extend and operate.
- Learn quickly when things are unfamiliar by prototyping, then hardening and documenting what you ship.
Bonus (not required)
- Experience with AdTech or other high volume real‑time systems
Who This Role Will Suit
This role suits engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving.
Distributed Team Note
We’re a distributed team with a growing engineering presence in India, so comfort with async collaboration and clear written communication is important.
We use agentic coding tools heavily (e.g. Cursorand Claude Code) to plan, scaffold, refactor, and debug production code, while maintaining strong engineering judgment and ownership of outcomes.
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.
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