AI Research Engineer - ML Engineering

Who we are

Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards.

As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously.

We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications.

The role

At Helsing, we are pioneering the future of autonomous decision-making for defence. Our work spans the full AI landscape, including high-volume data processing, RL agent training, and large-scale foundation models. As a member of a cross-functional team, you will architect and implement the tools and platforms that enable these breakthroughs. Your focus will be on abstracting complex distributed systems to maximise training throughput and developer velocity. We are looking for engineers who can navigate the convergence of machine learning and systems engineering to build robust, scalable platforms.

What you will do:

• Extend our highly integrated deep learning frameworks (built on top of PyTorch), making them efficient and easy to use for a wide range of use cases.

• Scale our current infrastructure and tooling stack to support faster and larger distributed training.

• Design data strategy to support large scale datasets and efficient storage, ensuring GPUs stay warm.

You should apply if you:

• Hold an MSc or PhD in Computer Science or STEM field, with a focus on Machine Learning and Deep Learning.

• Have strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX/TensorFlow). You don’t just import libraries; you are comfortable writing custom layers, loss functions, and distributed training loops.

• Are a clear communicator who can build from complex theoretical concepts and contribute to the company's internal engineering culture.

• Have a "first-principles" mindset: you enjoy reading the latest AI optimisation blog posts and integrating them into codebases rapidly.

• You have debugged production ML pipelines and can tell a good war story about finding a subtle numerical or performance issue.

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