Company Description

Calvin Risk provides innovative solutions for assessing and managing the risks associated with AI algorithms in commercial use. Our cutting-edge risk assessment framework and management platform enable organizations to build trustworthy and responsible AI systems. We empower businesses to maintain control over their AI initiatives while mitigating risks. Calvin Risk is dedicated to supporting ethical AI practices and ensuring reliability in AI deployments.

Role Description

This is a full-time hybrid role based in Zurich, with the flexibility for occasional remote work. As an AI Research Engineer, you will contribute to designing, developing, and implementing advanced algorithms for AI risk assessment and management. Your daily tasks will include researching state-of-the-art AI methodologies, building and training models, coding efficient software systems, and collaborating with cross-functional teams to enhance our technological solutions.

What you will do as an AI Research engineer:

 

·      Architect Risk Engines: Design and implement scalable methodologies to quantify AI risks (robustness, bias, explainability) and bake them into our core platform.

·      Battle-Test Models: Validate your frameworks against real-world AI incidents, refining them to withstand the chaos of production environments.

·      Technological Scouting: Stay at the bleeding edge of LLM evaluation, adversarial attacks, and interpretability, integrating the best tech into our product.

·      Customer-Centric Engineering: Translate complex mathematical risks into actionable metrics that align with real-world enterprise data and business needs.

·      Thought Leadership: Represent Calvin Risk at top-tier global conferences (NeurIPS, ICML) and corporate summits as the technical authority on AI safety.

 

Who you are:

·      A Pragmatic Researcher: You have a deep background in scientific methodology, but you’re driven by building products, not just publishing papers.

·      Technical Depth: You have significant experience in AI/ML development. If your GitHub or CV shows work in explainability (XAI), adversarial robustness, or uncertainty quantification, you’re at the top of our list.

·      Systems Thinker: You understand that "perfect" is the enemy of "deployed." You can balance rigorous scientific accuracy with the constraints of data availability and tight shipping cycles.

·      Collaborative Builder: You thrive in an interdisciplinary environment and are excited to wear multiple hats—from refining an algorithm to helping shape the product roadmap.