AI role · Open offer

Senior Multimodal ML Engineer — vision-language

DeepMind

Full-time
Senior
Multimodal
Hybrid
5 New Street Square, London, England EC4A 3TW, United Kingdom · Hybrid
London, United Kingdom
£130k–£175k + bonus + 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: 5 New Street Square, London, England EC4A 3TW, United Kingdom · Hybrid
Compensation: £130k–£175k + bonus + equity
Experience level: Senior
Team: Multimodal
Benefits & perks

About this role

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

DeepMind is hiring a Senior Senior Multimodal ML Engineer — vision-language on the Multimodal 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.

Google DeepMind advances multimodal research and applied systems from London.

Senior Multimodal ML Engineer: vision-language models for documents, images, and agents.

Missions

Train and evaluate VLMs with rigorous multimodal benchmarks.
Collaborate with product teams on grounded multimodal features.

Stack & environment

Python, PyTorch/JAX, CUDA, Docker.

What we look for

CV + NLP or VLM experience; research or applied ML depth.

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.
Computer Vision

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.
JAXTool
Regular use of JAX 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: VLM, captioning, or document understanding with measurable quality.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, Computer Vision, NLP, 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

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What the employer asked for

VLM, captioning, or document understanding with measurable quality.

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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