Graduate Engineering Programme

We're building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience.

We specialise in turning advances in sensing, AI, and communications into operational capability for the edge, where connectivity may be degraded or denied. Our work focuses on accelerating the deployment of technology, improving decision-making for frontline teams, and protecting people and critical assets in demanding environments.

Headquartered in Bristol, Rowden employs over 200 people and operates over 20,000 square feet of engineering and manufacturing facilities. Recognised in the Financial Times FT1000 as one of Europe's fastest-growing engineering businesses, there has never been a better time to join.

Engineering Graduates at Rowden

This is not a traditional graduate scheme built around a narrow role or a long, segmented programme. The Applied Graduate Programme is designed for engineers who want broad systems and product exposure, with real responsibility early and close engagement with end users and operational constraints.

Engineering graduates work on deployable systems supporting defence, emergency services, and national security organisations, alongside engineers from multiple disciplines.

You will join a rotational engineering programme designed to build technical breadth fast, supported by experienced mentors, structured feedback, and clear milestones. From the outset, you will be trusted with meaningful work and increasing ownership as your capability develops.

If you want to understand more about how we design and build systems at Rowden, it’s worth reading this blog from one of our Senior Principal Engineers: Rowden Technologies | Engineering Reflections on Rowden's Values

What you'll work on

As a Graduate Engineer at Rowden, the types of work you’ll be involved with include:

• Integrating sensor and data feeds into geospatial and situational awareness applications, working across software, systems, and interfaces.
• Designing and testing system behaviour under degraded communications, including store-and-forward, prioritisation, and security controls.
• Deploying and evaluating machine learning models for edge use cases such as computer vision or signal processing, with a focus on measurable performance and operational constraints.
• Building future variants of deployable sensing and communications products, influencing how those systems evolve through hands-on engineering, integration work, and performance data.
• Shipping software that enables edge devices to be deployed, configured, and sustained at scale.