AI role · Open offer

ML Research Intern — BenevolentAI internship

BenevolentAI

Internship
Junior
Drug discovery ML
Hybrid
40 Churchway, London NW1 1LW, United Kingdom · Hybrid
London, United Kingdom
Competitive internship stipend

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: Internship
Workplace: Hybrid
Location: 40 Churchway, London NW1 1LW, United Kingdom · Hybrid
Compensation: Competitive internship stipend
Experience level: Junior
Team: Drug discovery ML
Benefits & perks

About this role

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

BenevolentAI is hiring a Junior ML Research Intern — BenevolentAI internship on the Drug discovery ML 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 internship / Benevolent AI internships in London drug-discovery ML.

ML Research Intern (hybrid London): biomedical NLP, graph methods, and discovery modeling under mentorship.

Internship focus

Contribute to KG or biomedical NLP experiments with clear writeups.
Present results to scientists and engineers.

Stack & environment

Python, PyTorch, Jupyter, Docker.

What we look for

Strong ML coursework/projects; UK internship eligibility.

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.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
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.
JupyterTool
Regular use of Jupyter 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: Graph ML, biomedical NLP, or drug-discovery modeling coursework/projects.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, Machine Learning, and Docker.
You are early-career but can point to one end-to-end project (course, internship, or OSS) with honest metrics and what you would improve next.
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.
You read the full listing (comp: Competitive internship stipend, benefits in At a glance) and your expectations align.

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

Graph ML, biomedical NLP, or drug-discovery modeling coursework/projects.

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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