PRINCIPAL ENGINEER - AI
US - Remote
ABOUT WORLDLY
Worldly is the world’s most comprehensive impact intelligence platform — delivering real data to businesses on impacts within their supply chain. Worldly is trusted by 40,000 global brands, retailers, and manufacturers to provide the single source of ESG intelligence they need to accelerate business and industry transformation.
Through strategic and meaningful customer relationships, Worldly provides key insights into supplier performance, product impact, trends analysis, and compliance. When a company wants to change how business is done, we enable that systemic shift.
Backed by a dedicated global team of individuals aligned by values, Worldly proudly operates as a public benefit corporation with backing from mission-aligned investors. Want to learn more? Read our story http://worldly.io/about.
ABOUT THE ROLE
We are seeking a senior AI leader to help us expand our capabilities in predicting, analyzing, and mitigating environmental and social risk — across diverse, global, and often messy datasets. This role will guide how we apply cutting-edge AI/ML techniques to some of the world’s most pressing challenges, spanning pre-product R&D, core infrastructure design, and strategic product support.
As the Principal AI Engineer, you will operate as both a Doer and a Leader — someone who is eager to dive into the data, prototype novel solutions, and push the boundaries of our platform’s intelligence layer, while also shaping strategic direction and mentoring the next generation of applied data scientists at Worldly.
You’ll collaborate with engineering, product, and executive teams to design practical, scalable, and ethical AI solutions that deliver value across our core environmental and social modules. This is not a product management role — but you’ll be deeply involved in helping define what is possible, viable, and responsible when it comes to applying AI across 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
- Retrieval-augmented generation (RAG) and generative AI for sustainability use cases
- Get hands-on with data and models — prototyping solutions, validating hypotheses, and stress-testing assumptions through real experimentation.
- Mentor and provide technical guidance to junior data scientists and analysts across the organization.
- Partner with Engineering to design and validate the right tooling, pipelines, and infrastructure — from feature stores to model registries to evaluation frameworks.
- Partner with Product to shape early-stage AI concepts into viable, explainable features — not as a PM, but as a technical architect and strategic guide.
- Explore, evaluate, and adapt open-source models and framewo