Research Engineer (Evals)

Moonfire; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Equity

Hybrid work model

Office in London/Paris

Relocation support

Private health insurance

Mental health support

Job summary

White Circle in London is seeking a research engineer to build and maintain an internal benchmark suite spanning single/multi-turn content and agentic guardrails. You will collaborate with the core safety team to study agent behaviours and push evaluation tooling into production.

Applicants should have hands-on Python production experience, strong benchmark design skills, and a track record of shipping reliable code for ML systems. Hybrid work with offices in London and Paris is offered.

Qualifications

• Have built an LLM benchmark from scratch that distinguished specific model capabilities.
• Have built synthetic data for post-training textual or multimodal models.
• Can reproduce a published benchmark result and identify fragile methodologies.
• You write Python that other people can build on and ship to production.
• You can write efficient LLM inference setups with orchestration of parallel calls and retries.
• An AI power-user fluent with frontier models and coding agents day to day.

Responsibilities

• Own and maintain our internal benchmark suite for single/multi-turn content guardrails.
• Build benchmarks that distinguish model capabilities and feed into product evals.
• Adapt evals to new features and changing product data.
• Collaborate with product and research teams on evaluation tooling.
• Work on research projects studying agent behaviours in the wild.

Skills

Python

LLM benchmarking

Experiment design

Production-grade code

Distributed systems

Education

Master's degree in CS/AI

Tools

Docker

Git

PyTorch

Job description

TL;DR: We're looking for a research engineer to build and maintain our internal suite of benchmarks, covering single/multi-turn content and agentic guardrails. The research engineer would also work with the team on projects studying agent behaviours in the wild.

About us

White Circle is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.

• We’ve recently raised our Series A funding round, taking our total funding to $70M. Our investors include top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others
• We process over 100M+ API calls every month
• We fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model

We’re a small, highly focused team. If you want to work deeply on hard problems, see your work ship to production quickly, and influence how AI safety is actually built - you’re the one we need.

About the team

White Circle's fundamental research team works on the science of how AI systems fail in the real world: where agents break, how misalignment actually looks like to the end user, and how it is present in the model internals. We build the evals, benchmarks, environments, and tooling that empirically study high-impact agent reliability concerns - some of which become the guardrails shipped in our products, and some of which become public writeups.

What you’ll do

Own and maintain our internal benchmark suite, covering single/multi-turn content guardrails and agentic safety.

• Build benchmarks that distinguish specific model capabilities.
• Work with the product team to build evals covering core functionality of our flagship models.
• Build benchmarks for new features coming out of the research team.
• Adapt and extend evals to new verticals and changing product data.
• Work on research projects that study and quantify realistic agentic and LLM failure modes in the wild.

You’ll fit right in if you

• Have built an LLM benchmark from scratch that distinguished specific model capabilities (i.e., produced a measurable, defensible capability difference, not just a score).
• Have built synthetic data for post-training textual or multimodal models.
• Can reproduce a published benchmark result and identify where the original methodology is fragile or misleading.
• You write Python that other people can build on. Our whole stack is Python; we want someone who has shipped and maintained production code and who factors messy problems into clean abstractions others can extend.
• You can write efficient LLM inference setups, including sensible orchestration of parallel calls, retries, rate-limit handling.
• An AI power-user - fluent with frontier models and coding agents day to day.

A big plus

• Automated red-teaming experience
• Have worked across a range of agentic scaffolds and reproduced public benchmark results on them
• Strong knowledge of existing reward-model / monitoring / safety benchmarks
• One or more published papers in the evals / safety-evaluation space

Compensation & benefits

• Competitive compensation, including equity
• Flexible time off
• Office in central London/Paris with flexible hybrid setup
• Relocation support if you’re moving to Paris, available after your probationary period
• Premium private health insurance
• Mental health support, including coverage for therapy when you need it
• Lunch and dinner covered when you work from the office
• Learning and development support for courses, conferences, and opportunities to grow your skills
• All the hardware, subscriptions, tools, and services you need
• Team off-sites twice a year: we’ve recently been to the Alps, Saint-Tropez, and Marbella

Process

1. Intro call with Talent
2. Test assignment
3. Technical interview with Head of Fundamental Research
4. Final interview with CEO