AI Research Engineer - AI Safety

At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making. You will have the unique opportunity to shape AI capabilities in one of the most challenging sectors, where high generalisation capabilities need to be paired with hardware constraints and robustness against adversarial attacks.

You will join a team focused on AI Assurance, where you will develop cutting-edge techniques for scalable evaluation of AI products across the company, design data collection and experimentation strategies to extract causal insights, and enhance responsible decision-making via uncertainty quantification and safety mechanisms.

• Hold an MSc in Mathematics, Statistics, Machine Learning, or a closely related field, with a strong mathematical and statistical foundation.

• Have hands‑on experience in model evaluation, uncertainty quantification, or calibration. You understand the difference between epistemic and aleatoric uncertainty and know how to measure and reduce them in deep learning models.

• Are familiar with methods for distribution shift detection, out‑of‑distribution detection, and adversarial robustness evaluation, and can design experiments that surface genuine failure modes rather than benchmark artefacts.

• Possess solid software engineering skills, writing clean and well‑structured code in Python and/or languages like Rust or modern C++, and have experience deploying AI software to production including testing, QA, and monitoring.

• Have excellent communication skills and the ability to report and present research findings clearly and efficiently, both internally and externally.

• Are passionate about keeping up to date with current research and enjoy reimplementing and extending state‑of‑the‑art approaches in deep learning evaluation and assurance.

Note:

We operate at an intersection where women, as well as other minority groups, are systemically 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.

• PhD in model evaluation, uncertainty quantification, robustness, experimental design, causal inference, or a related field, with publications in top‑tier venues (e.g. NeurIPS, ICML, ICLR, CVPR).

• Previous industrial experience assuring the safe deployment of AI products in high‑stakes or safety‑critical systems.

• Familiarity with formal methods, interpretability techniques, or Bayesian approaches to reasoning about model behaviour under uncertainty.

• Experience with adversarial machine learning, red‑teaming, or systematic stress‑testing of AI systems in operational settings.

• Experience with conformal prediction, calibration methods (e.g. temperature scaling, Platt scaling), or Bayesian deep learning.

• 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…