Computer Vision Engineer

You’re likely an Applied AI Engineer, Computer Vision Engineer, or similar, and you:

• Have built multimodal or vision based agents using images and video, not just text
• Background in Computer Vision (YOLO, ResNET, EfficientNET etc)
• Familiar with Vision Agent and VLMs (LLaVA, CLIP, Flamingo etc)
• Have worked in similarly messy technical environments, automation, robotics, device control, autonomy, AV, or complex UI driven systems
• Comfortable working with QA’s and Testing Leads to understand their workflows
• Worked in a AI product start-up environment (ideally 0-1)

• Multimodal agents that reason over images and video, often across multiple frames, and decide what to do next
• Vision driven agents that interact with real games, tapping, swiping, clicking, navigating
• Systems that follow loops, observe, decide, act, recover, not fixed scripts
• Automation that runs reliably across devices, OS versions, screen sizes, and edge cases
• Production grade agent systems that people trust to run unsupervised

Think less “prompting” and more systems thinking.

Why this role is different

• Founding level ownership, you are shaping how the core agent system is built
• Real production constraints, not research theatre
• Agents operating in the wild, not controlled demos
• A product that lives or dies on reliability

If you like seeing your work break, fixing it, and making it stronger, you’ll enjoy this.

Founding Applied AI Engineer - Agentic Systems (Games) - upto £130k +Equity - Hybrid London

Built agents that actually act in the real world, not just predict?

Worked with multimodal models, images, video, vision, and turned them into systems that make decisions and recover when things go wrong?

Enjoy solving messy, real problems where reliability matters more than demos?

If that sounds like you, keep reading.

The challenge

This is an early stage startup building autonomous agents that test games the way real players do. Not scripted bots. Not brittle automation. Proper agents that observe gameplay, reason over images and video frames, and take actions across real devices.

The goal is simple to say and hard to execute: make studios trust agents more than manual QA.

You’ll be building multimodal agents that operate inside live games, across mobile and desktop, handling inconsistent UIs, timing issues, network lag, and all the things that break naive automation.

This is about getting agents out of notebooks and into production.