TL;DR

AI Research Engineer (AI Safety): Develop and evaluate AI capabilities focusing on uncertainty quantification, robustness, and safety mechanisms in AI systems. Focus on designing scalable evaluation techniques, causal insights extraction, and ensuring AI reliability in adversarial and real-world scenarios.

What you will do

Define operational domains and evaluate reliability of in-house AI capabilities.
Develop and extend state-of-the-art methods in uncertainty quantification and calibration.
Understand, interface with, and evaluate robustness of AI systems in real-world and adversarial settings.
Collaborate across teams to contribute to impactful AI assurance projects.

Requirements

STEM MSc with strong mathematical and statistical background.
Excellent communication skills for clear internal and external reporting.
Passion for current AI research and ability to reimplement/extend deep learning approaches.
Solid software engineering skills in Python and/or Rust, Java, or modern C++.
Experience deploying AI software to production including testing, QA, and monitoring.

Nice to have

PhD in model evaluation, robustness, uncertainty quantification, experimental design, causal inference, or related fields.
Publications in top-tier AI conferences and journals.
Industrial experience assuring safe deployment of AI products.
Experience with AI software production deployment including testing and monitoring.

Culture & Benefits

Focus on outcomes rather than time-tracking.
Competitive compensation and stock options.
Relocation support.
Social and education allowances.
Regular company events and all-hands across Europe.
Hands-on onboarding program to learn tools and ML pipelines from day one.