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 roleYou'll build Refractal's data foundation, including the evidence store that customers' auditors rely on, the analytics behind compliance reporting and the datasets our models are trained on.
What you'll doDesign the data model for our append-only, cryptographically signed evidence store.
Build the streaming and batch layer that turns ingested activity and detections into queryable data, working with our integrations engineers on schemas.
Own data quality, retention, residency and deletion controls for regulated customers.
Build training and evaluation datasets with our data scientists and security engineers.
Make compliance reporting against the EU AI Act, NIST AI RMF and ISO/IEC 42001 quick to produce from our data.
The evidence store is in production, documented and trusted by customers' auditors.
Colleagues answer compliance and detection questions from data you own, without ad hoc work.
Training and evaluation data has clear provenance and separation.
6+ years in data engineering, owning production pipelines.
Strong Python and SQL, with Postgres plus a warehouse or analytics store such as BigQuery or ClickHouse.
Experience with streaming (Kafka, Pub/Sub or similar) and orchestration (Airflow, Dagster or similar).
Data modelling for audit trails, lineage and multi-tenant isolation.
Security-minded handling of sensitive data.
Data work in banking, insurance or other regulated industries.
Experience with UK GDPR and data residency.
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.
Compensation and benefitsSalary: £80,000–£135,000
Competitive equity to share in Refractal's growth
Pension contribution
Private medical and dental cover
Visa and relocation support
Skills Required
- 6+ years of experience in data engineering, owning production pipelines
- Strong Python and SQL skills
- Experience with PostgreSQL plus a warehouse or analytics store such as BigQuery or ClickHouse
- Experience with streaming technologies such as Kafka or Pub/Sub
- Experience with orchestration technologies such as Airflow or Dagster
- Data modeling experience for audit trails, lineage, and multi-tenant isolation
- Security-minded handling of sensitive data
- Data engineering experience in banking, insurance, or other regulated industries
- Experience with UK GDPR and data residency
What We Do
Refractal builds security infrastructure for high-assurance AI, helping governments and enterprises test AI agents before deployment, protect them in production, and meet compliance requirements. Its software monitors agent activity and enforces policies at the point of action, evaluating tool calls and data access and recording tamper-evident audit trails. The platform integrates with existing telemetry and observability tools and helps detect AI-enabled threats across models, tools, and workflows.
