AI Research Intern (PhD) – 3D Computer Vision

You will work closely with a team distributed across Europe, alongside globally renowned AI experts, while collaborating with a broader team across Europe.

The day-to-day

• Survey and critically assess state-of-the-art research in geometric computer vision, feature matching, place recognition, and SLAM/VIO.
• Conceptualise how emerging techniques could apply to our localisation and reconstruction pipelines, and discuss trade-offs with the team.
• Evaluate the strengths and weaknesses of different algorithmic approaches for concrete, mission-critical use cases.
• Prototype and benchmark promising concepts against real-world operational conditions such as changes in illumination, viewpoint, season, or sensor modality.
• Summarise findings in clear, concise written reports and present them to the team.

You should apply if you

• Are currently pursuing a PhD in computer vision, machine learning, robotics, or a closely related field.
• Have demonstrated theoretical and practical experience in localisation, scene matching, SLAM, visual odometry, or 3D reconstruction .
• Are proficient in Python and experienced with AI frameworks such as PyTorch.
• Have experience with Rust or C++ .
• Are able to work reliably and independently, and enjoy turning research ideas into working code.

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.

Nice to have

• Published a paper, ideally on applied computer vision, at a top-tier conference or journal (e.g. CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA, IROS).
• Experience with learned feature matching, place recognition, VIO, or neural 3D representations (e.g. NeRF, Gaussian Splatting).
• Experience with model compression or domain adaptation for compute-constrained or edge deployment.
• Experience with simulators, emulators, or synthetic data generation for geometry or localisation tasks.

Join Helsing and work with world-leading experts in their fields

• 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.

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.

Helsing's Candidate Privacy and Confidentiality Regime can be found here .

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung