We're building technology for the next generation of defence and autonomous systems.
The problems we're solving sit across computer vision, machine learning, sensors, robotics and algorithms , with everything we build ultimately being used in the real world.
We're looking for a Computer Vision / ML Engineer to join our growing AI team.
This is a hands-on engineering role for someone who enjoys taking difficult, ambiguous problems and turning them into working systems that actually get deployed .
A lot of what you'll work on won't have a ready-made solution. You'll need to experiment, build, test and iterate — combining modern ML with traditional computer vision and algorithms to find an approach that works.
Why you should join us
You'll solve problems that haven't already been solved
We're not asking you to spend your time implementing standard ML pipelines or fine-tuning models against problems that have already been solved a thousand times.
Our customers come to us with complex real-world problems , often involving imperfect sensor data, unusual environments and significant constraints.
Your job is to figure out how to solve them.
Sometimes the right answer will be a modern ML model.
Sometimes it will be a clever algorithm.
Often it'll be a combination of both.
We're interested in engineers who care about finding the best solution, rather than forcing every problem into the latest AI technique.
You'll build things that go into production
This isn't a research role where your work disappears into a paper or stays as a prototype.
The things you build will become part of production systems used by real customers .
You'll take problems from an initial idea through experimentation, implementation, testing and deployment.
You'll work closely with software engineers and other AI specialists to make sure what you build actually performs reliably outside of a controlled environment.
That means thinking about far more than model accuracy.
You'll need to consider performance, reliability, data, compute constraints and how your system behaves when the real world inevitably doesn't look like your training data.
You'll see your work in the real world
You'll also work directly with customers.
Rather than receiving a requirements document and disappearing into the engineering team, you'll have the opportunity to understand the problem first-hand, build a solution and then go and see it being used.
That means some travel to customer sites in the UK and occasionally internationally.
You might spend one week developing and testing an approach in the office, then the next week deploying it and seeing how it performs in the environment it was actually designed for.
The feedback loop is fast — and that's where a lot of the interesting engineering happens.
You'll have huge ownership
We're building out the AI capability of the company and you'll be joining at an early stage.
You'll have a significant amount of freedom over how you approach problems and the opportunity to influence the technical direction of the team.
You'll work alongside strong engineers across AI, software, robotics and systems , giving you exposure to problems well beyond a typical ML engineering role.
If you want to become the person who can take an ambiguous problem, work out the technical approach and actually get it working in production , this is a great environment to do it.
What we're looking for
We're looking for strong engineers who happen to specialise in ML and computer vision.
Computer Science, AI, Machine Learning, Robotics, Engineering or a related technical subject
machine learning with traditional algorithms and engineering techniques
Experience with sensor fusion, simulation, synthetic data, MLOps or deploying models to resource-constrained hardware would be useful, but isn't essential.
A PhD or academic publications are not required . We're interested in what you can build and how you approach difficult problems.
We're looking for someone who gets excited by problems where there isn't an obvious answer .
If you want to combine ML, computer vision and algorithms to build things that haven't been done before — and then see those things actually deployed in the real world — we'd like to hear from you.
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