Refractal
Software Engineer

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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 connect Refractal to the security systems our customers already use, building the integrations and data pipelines that make their security data usable. You'll work closely with our security engineers and with customers to understand their environments and turn their requirements into reliable software.
What you'll do
- Build and maintain integrations with security and observability tools, including platforms such as Splunk.
- Develop pipelines to ingest, validate, and transform security data, accounting for inconsistent formats, incomplete records, and changing source systems.
- Design clear interfaces between integrations and the wider product, working with platform engineers to keep responsibilities well defined.
- Make integrations reliable in production through testing, monitoring, useful error messages, and safe handling of credentials and sensitive data.
- Investigate customer integration issues and turn recurring problems into product improvements, documentation, and reusable implementation patterns.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Success in your first six months
- You've shipped and supported priority integrations that customers can use with clear setup guidance.
- The data flows you own handle common failures predictably and make problems straightforward to diagnose.
- You've established reusable patterns that make the next integration easier to build and maintain.
About you: essential
- Experience building and maintaining software, with a clear approach to testing, debugging, and code review.
- Familiarity with data engineering concepts, including ingestion, transformation, data quality, and pipeline reliability.
- Experience working with APIs and external systems, including authentication, pagination, rate limits, and error handling.
- Ability to investigate unfamiliar systems and turn incomplete requirements into a practical implementation.
- Clear communication and a willingness to own work through deployment and ongoing support.


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Nice to have
- Experience integrating with Splunk or other SIEM and observability platforms.
- Familiarity with security telemetry, event schemas, or detection workflows.
- Experience with high-volume data processing or customer-facing integration projects.
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: £60,000–£115,000
- Competitive equity to share in Refractal's growth
- Pension contribution
- Private medical and dental cover
- Visa and relocation support
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