UK remote
MTS: AI Research Engineer
About this role
Type: Full Time, Hybrid Team: Research Focus: AI & Agent Systems Who We Are We're a team of founders, engineers, researchers, creatives, and operators building what we believe will be one of the defining companies of the AI era. Some of us came from startups. Some from academia. Some from hospitality, defense, design, or completely different worlds. We don’t care how you got here - we care about your energy, intelligence, curiosity, resilience, and ambition.
Who We Are Not We are not remote. We value the spontaneous moments that happen when we’re together - over dinner, in the hallway, around whiteboards. We like being around each other because the best conversations here rarely happen in meetings. We are not your typical team. It’s not just the 10 countries we’re from, the languages we speak or our non-traditional backgrounds. We’re solving hard problems with people we respect - we make the most of our time together, we make it special.
We are not another “polished” SF startup . We value velocity over speed, raw talent over brands, and genuine human connection above all else. We value being successful, not looking successful. What You’ll Own & Build As a Member of Technical Staff within the Research Tribe, you’ll be one of the early engineers shaping the core systems that power Seldon. You won’t just build agents - you’ll design the infrastructure that governs how they operate: how they coordinate, how they’re evaluated, and how their behavior improves over time .
This is a deeply technical role bridging distributed systems, machine intelligence, and product - turning stochastic models into reliable, production-grade systems. Multi-Agent Systems Architecture: Design and implement agentic systems, rather than prompt chains, at scale. You’ll define agent topologies, robust orchestration layers, structured state management, persistent and scoped memory, tool-use registries and execution, and build structured, inspectable systems.
You’re building a long-running harness-based agentic platform, not wiring together LLM API calls. Behavioral & Persona Modeling: Simulate user archetypes with goal-directed behavior. You’ll design data-grounded personas, enable “computer-use” on real interfaces, and develop agents with critique and reflection loops. You’re producing measurable, actionable behavior. Evaluation & Prompt Engineering: Design prompt schemas and reasoning templates as integrated systems.