About the job
Principal Engineer - AI
Remote - United States
About Worldly
Worldly stands as the premier impact intelligence platform, equipping businesses with actionable data regarding their supply chain impacts. Trusted by over 40,000 global brands, retailers, and manufacturers, we provide the essential ESG intelligence required to spearhead business and industry transformation.
Through the cultivation of strategic and meaningful customer relationships, Worldly offers critical insights into supplier performance, product impact, trend analysis, and compliance. We empower companies to initiate fundamental changes in how business operates.
Backed by a global team of dedicated individuals united by shared values, Worldly operates proudly as a public benefit corporation, supported by mission-driven investors. Interested in learning more? Discover our journey.
About The Role
We are on the lookout for a senior AI leader to enhance our capabilities in predicting, analyzing, and managing environmental and social risks across diverse, complex datasets. This role will shape how we leverage state-of-the-art AI/ML techniques to tackle some of the most urgent global challenges, encompassing pre-product R&D, core infrastructure design, and strategic product support.
As the Principal AI Engineer, you will function both as a Doer and a Leader — diving into data, prototyping innovative solutions, and pushing the limits of our platform’s intelligence layer, while also defining strategic directions and mentoring emerging data scientists at Worldly.
You’ll partner with engineering, product, and executive teams to engineer practical, scalable, and ethical AI solutions that yield value across our core environmental and social modules. While this is not a product management role, you will play a key part in defining what is achievable, feasible, and responsible in the application of AI within the Worldly ecosystem.
What You'll Do
- Design and lead AI/ML strategy for high-impact domains such as:
- Environmental risk modeling (climate, water, energy, etc.)
- Social impact analytics (worker voice, audit parsing, wellbeing metrics)
- Supply chain data fusion and predictive analytics

