Location: Remote (India or UK preferred)
Type: Full-Time | Permanent
Tech: Python and/or TypeScript, FastAPI/Node, Kafka/SQS, AWS, Terraform/CDK, AI stack
Comp: Competitive salary + meaningful equity

Why This Role Exists

PubX builds publisher-first agentic advertising infrastructure — AI that makes real-time, revenue-critical pricing decisions for digital publishers. Our dynamic floor pricing engine uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We’re 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 static or 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 set optimal price floors per auction, in real-time. As we expand into agentic AI systems, we’re building the next generation of autonomous advertising infrastructure.

About us

We’re a ~25-person, fully remote team founded in 2020 by Andrew Mole (CEO) and Alex Rosen (CTO), who previously built Platform360 — a machine-learning powered DSP — giving them a decade of deep AdTech experience. We’re seed-funded, profitable in our core product, and growing our engineering team with a strong presence in India and UK leadership. Our investors include Concept Ventures, Haatch, and Force Over Mass Capital.

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.

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., Cursor and Claude Code) to plan, scaffold, refactor, and debug production code, while maintaining strong engineering judgment and ownership of outcomes.

Interview Process

Our process is designed to be practical and respectful.

1. CV & Profile Review – Relevant experience and background
2. Initial Chat (30 mins) – Motivation and role fit
3. Technical Interview (60 mins) – Architecture, design choices, and real scenarios
4. Practical Exercise + Discussion (60 mins) – A small task related to the role

What We Offer

• Competitive salary with meaningful equity
• Fully remote, async-friendly working
• Supportive, low-ego engineering culture
• Budget for learning and professional development

If you’re interested in building and improving real systems in a growing product company, we’d love to hear from you.