Role Description
About Refractal
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 role
You'll act as technical lead on Refractal's US deals from discovery through evaluation, then work inside customers' security, risk and AI teams to get Refractal into production.
What you'll do
- Lead technical discovery, demonstrations and evaluations alongside our Account Executives.
- Deploy Refractal into customers' environments and integrate it with their AI agents, models and security tooling.
- Translate customer policies and regulatory requirements into enforceable controls.
- Help managed security service providers deploy Refractal for their own clients.
- Build connectors and reference implementations, and bring US customer patterns back into the roadmap.
Success in your first six months
- Evaluations you led have turned into signed customers.
- Those customers are in production and rely on Refractal day to day.
- Reusable demonstration and deployment assets make the next US deal faster.
About you: essential
- 6+ years in software engineering, including 2+ in customer-facing roles such as forward deployed, solutions or sales engineering.
- Strong Python, and confidence across APIs, cloud and identity.
- Hands-on experience building with LLMs and AI agents.
- Security experience as a practitioner or at a security vendor.
- Credibility with CISOs and engineers alike, and willingness to travel.
Nice to have
- Experience with US financial services or healthcare customers.
- Pre-sales experience at an early-stage security company.
Our values
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 benefits
- Salary: $170,000–$250,000 base
- Competitive equity to share in Refractal's growth
- Pension contribution
- Private medical and dental cover
- Visa and relocation support
Compensation Range: $170K - $250K
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
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