Refractal is an AI security company building the infrastructure organisations need to defend against autonomous AI threats. 2026 has shown that generally capable intelligence is here. The challenge now is modernising security to stop AI-enabled threats, whether they come from internal agents going rogue or from attackers using AI to become more dangerous.
Refractal was founded on cybersecurity and AI expertise from MIT, Microsoft, NASA and the U.S. Navy. Our product lets organisations detect AI-enabled threats at low cost without sacrificing detection quality. We take a practical approach to AI risk, combining proven cybersecurity methods with frontier AI security research.
Today, we work with governments and organisations that can't afford to get security wrong. We're backed by leading deep-tech and cybersecurity investors and are growing the team to deliver on this mission.
The roleOwn the services and infrastructure that keep Refractal secure and reliable in production. You will work across the enforcement runtime, data integrity, tenant isolation, agent tracing, model services, and cloud infrastructure.
Build systems that connect activity across agents, services, and tools, evaluate risk, apply decisions, and preserve the evidence behind them.
What you'll work onBuild and operate a low-latency runtime enforcement pipeline with predictable failure behaviour.
Own observability and tracing across agent actions, tool calls, model calls, and enforcement decisions.
Build tamper-evident evidence storage and enforce multi-tenant isolation throughout every data path.
Create reliable event and streaming services with safe handling for load, retries, backpressure, and disconnections.
Build infrastructure for red-team jobs, evaluation workloads, and model-serving services.
Own cloud infrastructure, continuous delivery, secrets, artefact verification, and production trust boundaries.
A systems engineer who reasons carefully about latency, failure, isolation, integrity, and cost, and uses measurements rather than assumptions.
Strong experience with backend, distributed, or infrastructure systems; Python experience is preferred.
Production observability and distributed tracing experience, ideally with OpenTelemetry or similar tooling.
Experience building responsive services under load with correct timeout, retry, and backpressure behaviour.
Experience with Postgres, Row-Level Security, migrations, and multi-tenant access-control testing.
Hands-on experience with containers, cloud infrastructure, continuous delivery, and operational security.
The ability to work independently and own technically ambiguous production problems.
Radical transparency. We believe the best work happens when everyone feels able to give and receive honest, constructive feedback.
Public service. We see AI security as a form of public service. Getting the AI transition right means putting the right infrastructure in place to manage the threats that come with it, and our work helps protect the institutions and services society depends on.
Fail fast. We test ideas early, seek feedback and learn quickly. We experiment rapidly and change course when something isn't working, without compromising on security.
Pragmatism, not dogmatism. We choose approaches based on how well they solve the problem. That means combining established cybersecurity methods with frontier AI research, questioning our assumptions, and changing our minds when the evidence changes.
What we offerCompetitive salary, meaningful equity, and a performance-linked bonus.
Direct influence over the product, architecture, engineering standards, and company direction.
A clear path to technical leadership as the engineering team grows.
London-based, in-person work, with UK Skilled Worker visa sponsorship and support for Global Talent applications.
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