Senior ML Engineer (Agentic AI) in London

Why This Role Exists

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.

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

Tech: Python, PyTorch, scikit-learn, MLflow/W&B, DataFrames, SQL, AWS (SageMaker/Bedrock)

• Design, train, evaluate, and deploy ML models that power real-time pricing decisions across billions of ad auction events
• Build and maintain production ML pipelines end-to-end: feature engineering, training, validation, deployment, monitoring, and retraining
• Develop and operate MLOps infrastructure (experiment tracking, model registry, A/B testing frameworks, automated retraining) on AWS using infrastructure-as-code
• Integrate LLM and agentic AI components into product workflows, including prompt engineering, orchestration, evaluation, and feedback loops
• Build feature pipelines and serving layers that operate at low latency and high throughput, working closely with data and backend engineers
• Own model observability and reliability: drift detection, performance monitoring, alerting, SLAs, and post-incident reviews
• Contribute to backend services and data pipelines where needed to close the gap between model development and production delivery

What We’re Looking For

• Strong ML engineering fundamentals: model development, feature engineering, evaluation methodology, and a solid understanding of when (and when not) to apply ML
• Hands-on experience deploying and operating ML models in production, including managing model lifecycle, versioning, A/B testing, and monitoring for drift and degradation
• Experience building MLOps tooling and infrastructure (e.g., MLflow, W&B, SageMaker Pipelines, Kubeflow, or similar) to support reproducible, automated workflows
• 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
• Experience integrating LLM/agentic AI components into production systems, including evaluation, grounding, and feedback capture
• Working knowledge of backend engineering: APIs, async processing, and containerised deployments — enough to ship models as reliable services

• 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.

• Experience with AdTech or other high volume real-time systems

Who This Role Will Suit

Company Benefits

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