AI role · Open offer

Research Engineer — LLM evaluation at scale

Market index

Focus: Climate AI

Paris · 100-300 employees · Scale-up · GenAI: No

Full-time
Senior
R&D
Hybrid
Cambridge, United Kingdom · Hybrid
£90k–£115k

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Cambridge, United Kingdom · Hybrid
Compensation: £90k–£115k
Experience level: Senior
Team: R&D
Benefits & perks

About this role

What the team is looking for and how this position fits.

Kayrros is hiring a Senior Research Engineer — LLM evaluation at scale on the R&D team (Hybrid · Cambridge, United Kingdom). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Cambridge-adjacent R&D cell partners with product teams across Europe to institutionalise evaluation: harnesses, judge calibration, and dataset governance before models reach customers.

Hybrid; willingness to publish internal tech notes (not necessarily public papers).

What you will do

Implement reusable eval SDKs, CI adapters, and dashboards for cross-team adoption.
Design human-in-the-loop programmes with vendor management and quality SLAs.
Quantify judge reliability vs human gold sets; recommend when to block releases.
Collaborate with safety on adversarial coverage maps.
Train engineering teams on experiment hygiene and statistical power basics.

What we look for

Strong Python engineering plus stats/ML depth.
Experience shipping eval infrastructure—not one-off Kaggle-style scripts.
Excellent writing for cross-functional alignment.

Nice to have

LangSmith/Weights & Biases style tooling familiarity.
Open-source eval dataset contributions.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
OpenAI API

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Describe a time human labels disagreed and how you resolved it.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Cambridge, United Kingdom)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: £90k–£115k, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Describe a time human labels disagreed and how you resolved it.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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