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- Tasks: Build autonomous agents that interact with real games and solve complex problems.
- Company: Exciting early-stage startup focused on innovative AI solutions.
- Benefits: Competitive salary up to ÂŁ150k, equity options, and hybrid work model.
- Why this job: Shape the future of game testing with cutting-edge technology and real-world impact.
- Qualifications: Experience in computer vision and multimodal agent development required.
- Other info: Founding level ownership with excellent growth potential in a dynamic environment.
The predicted salary is between 43200 - 72000 ÂŁ per year.
Founding Applied AI Engineer – Agentic Systems (Games) – upto £150k +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.
What you’ll be building
- 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.
About you
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)
Interested? If you’ve built agents that actually do things and want to take real ownership, get in touch.
Computer Vision Engineer employer: MBN Solutions
Contact Detail:
MBN Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Computer Vision Engineer
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and gaming industry. Attend meetups, webinars, or even just grab a coffee with someone who’s already in the field. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your projects, especially those involving multimodal agents or computer vision. Share it on platforms like GitHub or your personal website, and don’t forget to link it in your applications.
✨Tip Number 3
Prepare for technical interviews by brushing up on your problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. Remember, they want to see how you think and tackle real-world problems!
✨Tip Number 4
Apply through our website! We’re always on the lookout for talented individuals like you. Make sure to tailor your application to highlight your experience with building reliable systems and working in messy environments. Let’s get you in the door!
We think you need these skills to ace Computer Vision Engineer
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let your enthusiasm for AI and computer vision shine through. We want to see that you’re not just ticking boxes but genuinely excited about building agents that can operate in the real world.
Highlight Relevant Experience: Make sure to showcase any experience you've had with multimodal models or vision-based agents. We’re looking for specific examples of how you've tackled messy problems in previous roles, so don’t hold back!
Be Clear and Concise: While we love a good story, keep your application clear and to the point. Use straightforward language to explain your skills and experiences, making it easy for us to see why you’d be a great fit for the role.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity to shape the future of autonomous agents!
How to prepare for a job interview at MBN Solutions
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like YOLO, ResNET, and EfficientNET. Brush up on your knowledge of multimodal models and how they apply to real-world scenarios, especially in gaming.
✨Showcase Your Problem-Solving Skills
Prepare examples from your past experiences where you tackled messy technical challenges. Be ready to discuss how you approached these problems, what solutions you implemented, and how you ensured reliability in your systems.
✨Understand the Company’s Vision
Research Agentic Systems and their approach to building autonomous agents. Familiarise yourself with their goals and challenges, so you can demonstrate how your skills align with their mission during the interview.
✨Engage with Real Scenarios
Think about how you would apply your skills in a live game environment. Prepare to discuss how you would handle issues like inconsistent UIs or network lag, and be ready to suggest innovative solutions that go beyond traditional automation.
