Build the knowledge layer that sits underneath Superads' AI agents — the systems that give them genuine understanding of a brand rather than just access to its documents.
Design how brand knowledge is represented, stored, and continuously updated: the structures that let an agent reason about a brand the way a senior strategist would, not just retrieve what's been written down.
Develop agentic search capabilities — systems that let agents reason about what they don't know, identify knowledge gaps, and navigate information dynamically rather than relying on fixed retrieval pipelines.
Evaluate and iterate on architectures for knowledge retrieval and reasoning, identifying where current approaches break down and driving more interesting solutions.
Translate emerging AI research and patterns into production-ready systems that measurably improve output quality and reliability.
Work closely with the product and AI engineering teams to ensure the knowledge layer directly improves what users experience — staying connected to product impact even when the work takes you deep into infrastructure.

8+ years in high-performance engineering environments — ideally at companies pushing the frontier of enterprise AI, legal AI, or knowledge-intensive agent systems.
Proven experience building agentic workflows and RAG systems in production, with a focus on making AI output trustworthy and reliable — not just functional.
Deep understanding of how language models represent and use knowledge, well beyond prompt engineering or standard retrieval patterns. You've thought seriously about memory architectures, reasoning under uncertainty, and dynamic retrieval.
Experience with LangChain or LangGraph (or equivalent) for building multi-agent systems; strong Python skills, ideally with FastAPI.
Hands-on experience with a major cloud data platform — Snowflake, BigQuery, or Databricks — and comfort designing architectures that handle large data volumes reliably.
Strong opinions about where current AI approaches break down, formed through real production experience rather than theory.
Genuine curiosity about the brand intelligence problem — what it means to encode creative judgment, how you represent things that were never written down, how you know when a model's understanding is actually right. You don't need a marketing background, but you should find these questions interesting.
You take ownership of outcomes, communicate clearly when something isn't working, and follow the problem rather than your specialisation.

Diversity, Equity and Inclusion: We’re an equal-opportunity company. All applicants will be considered regardless of ethnicity, appearance, religion, gender identity, sexual orientation, national origin, veteran or disability status.

Staff AI/Data Engineer

Key skills