Member of Technical Staff, AIUp to £200,000
The role
Most enterprise AI stalls because models cannot be trusted to act autonomously inside production systems. This stealth team is solving the harness and safety layer directly, backed by founders who previously scaled enterprise AI and agentic security products from zero to eight figures. With 11 people, secure early funding, and active enterprise design partners, they are building production infrastructure rather than research demos, with a founder culture that commits to backing its own alumni when they eventually launch companies.
As one of two founding Member of Technical Staff seats in London, you will own applied AI and agent architecture end to end. This is not prompt wrapping or chaining basic API calls; it is core systems engineering around model indeterminism, autonomous tool use, and closed-loop evaluation. You will set technical direction with founders who want strong, unconventional technical opinions and zero corporate process.
The work
- Architect autonomous agent harnesses capable of executing complex workflows inside enterprise environments.
- Design safety guardrails and runtime constraints that manage model indeterminism without crippling agent utility.
- Build closed-loop evaluation systems that continuously benchmark and systematically improve harness performance.
- Ship agent features directly into production with large enterprise design partners.
- Contribute to foundational technical strategy, from memory management to multi-agent orchestration.
What you bring
- At least one year of hands-on experience building and deploying autonomous agents into real production environments, beyond prototyping with LLM APIs.
- Demonstrated harness engineering depth across orchestration, tool use, memory systems, and systematic eval pipelines.
- Either strong ML research foundations applied to agent architectures or senior applied AI experience paired with full-stack execution.
- A clear preference for engineering guardrails around model indeterminism rather than stripping agents back to rigid, deterministic rules.
- Based in or able to work regularly from the London King's Cross R&D hub.
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