AI Research Engineer - ML Engineering

About 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, with a focus on abstracting complex distributed systems to maximise training throughput and developer velocity.

What you'll 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.

What you'll need

• MSc or PhD in Computer Science or STEM field, with a focus on Machine Learning and Deep Learning.
• Strong software engineering skills in Python and fluency with modern DL frameworks (PyTorch/JAX/TensorFlow) — comfortable writing custom layers, loss functions, and distributed training loops.
• Clear communicator who can build from complex theoretical concepts and contribute to the company's internal engineering culture.
• A "first-principles" mindset: you enjoy reading the latest AI optimisation blog posts and integrating them into codebases rapidly.
• Experience debugging production ML pipelines, including subtle numerical or performance issues.

Nice to have

• Hands-on experience training models on large-scale GPU clusters, implementing advanced parallelism strategies, and understanding cross-node communication patterns (NCCL, MPI).
• Experience with large-scale datasets of different modalities, including tradeoffs in locality, encoding, formats, and streaming strategies.
• Proficiency with workload orchestrators like Slurm, Kubernetes, or Ray at scale.
• Low-level GPU architecture knowledge: memory hierarchies, warp execution, and training versus inference workload suitability.

What they offer

• Competitive salary and stock options (ESOP)
• Relocation support: up to €2,500 and 4 weeks temporary accommodation
• €500 yearly learning allowance
• Gym membership and mental health support (Nilo.health)
• Enhanced parental leave: 22 weeks fully paid for primary caregivers, 6 weeks for secondary caregivers
• Hands-on onboarding program ("AI-duction") working with AI and engineering teams from day one