AI role · Open offer

Knowledge Graph Engineer — BenevolentAI careers

BenevolentAI

Full-time
Senior
Knowledge graphs
Hybrid
40 Churchway, London NW1 1LW, United Kingdom · Hybrid
London, United Kingdom
£85k–£120k + equity

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.

4 skills2 tools

At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: 40 Churchway, London NW1 1LW, United Kingdom · Hybrid
Compensation: £85k–£120k + equity
Experience level: Senior
Team: Knowledge graphs
Benefits & perks

About this role

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

BenevolentAI is hiring a Senior Knowledge Graph Engineer — BenevolentAI careers on the Knowledge graphs team (Hybrid · London, United Kingdom). This is a hands-on role in NLP & language AI—not a generic “AI enthusiast” post.

You will own language understanding, generation, and quality metrics 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.

BenevolentAI — BenevolentAI careers / Benevolent AI jobs for knowledge-graph engineering in London.

Knowledge Graph Engineer: ontologies, graph ML, and clinical knowledge pipelines.

Missions

Scale biomedical knowledge graphs with quality checks.
Partner with ML scientists on graph features for discovery.

Stack & environment

Python, Neo4j, NLP stacks, Docker.

What we look for

KG or biomedical data experience; UK work auth.

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.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.
NLP

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.
Neo4jTool
Regular use of Neo4j in the NLP & language AI stack—configuration, debugging, and team conventions.

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: KG construction, biomedical ontologies, or graph ML at scale.
Several of these show up in recent shipped work—not only on your CV: Python, SQL, NLP, Machine Learning, and Docker.
You have owned language understanding, generation, and quality metrics in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · London, 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.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

KG construction, biomedical ontologies, or graph ML at scale.

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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