Staff AI Research Engineer - ML Engineering
Helsing · Berlin; London; Munich ·
- Work mode
- Remote
- Seniority
- Staff
- Employment
- Contract
- Category
- AI engineering
Helsing · Berlin; London; Munich ·
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Build and scale deep learning frameworks on PyTorch for distributed training, design data strategies for large-scale defence AI systems across GPU clusters.
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.
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.
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.
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.
Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points.
You have hands-on experience training models on large-scale GPU clusters, implementing advanced parallelism strategies, and understanding the underlying cross-node communication patterns (NCCL, MPI).
You have worked with large-scale datasets made of different modalities. Understanding the tradeoffs in locality, encoding, formats, and streaming strategies.
You are proficient with workload orchestrators like Slurm, Kubernetes, or Ray, having used them to schedule and manage complex, concurrent training jobs at scale.
You understand GPU architecture at a low level: memory hierarchies, warp execution, and what makes a GPU suited for training versus inference workloads.
Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns.
The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world.
Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward.
In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts.
We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about.
A hands-on onboarding program (affectionately labelled “AI-duction”), in which you will be familiarising yourself with our tools and ML pipelines used across the company. This is your opportunity to learn our tech stack, explore the company, and learn how we get things done - all whilst working with other AI and engineering teams from day one
These are the core benefits across all locations, there may be additional benefits in certain locations.
Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation.
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