Staff Engineer, Data and AI

Change.org is searching for a Staff Engineer, Data and AI Enablement to build and scale the data and AI platform behind Change.org powering trusted insights, personalization, and AI-enabled experiences that drive greater impact for millions of people creating change). You will report to our Senior Director of Data Engineering. As a key member of our Data and AI Enablement team, you’ll partner with teams across the organization to build and scale the platform, pipelines, architecture, and tooling that powers features, experimentation, and trusted decision-making.

Change.org is the world’s largest platform for democracy.

At a time when dissatisfaction with democracy globally is at an all-time high, we’re investing heavily in using AI to build the most powerful tools in the world to give people greater voice, while bringing people across the political spectrum together to identify shared solutions.

Our core petitions platform, used by more than 100 million people annually, is growing rapidly, and we are rebuilding it from the ground-up using new tools to turn anyone into a powerful civic leader on the issues they care about. Soon we will launch a new platform to identify the >80% of issues that most people agree on, locally and nationally, and to mobilize hundreds of millions of people to advocate for those common-ground solutions.

To realize this vision, we’re expanding the most talented team in the world at the intersection of technology and social impact - all focused every day on building healthier democracies globally.

• 7+ years of software engineering experience, with significant experience building distributed systems, data platforms, ML platforms, or comparable production infrastructure.

• Hands-on experience building and operating large-scale batch and/or streaming data systems, ideally including Kafka, Spark, workflow orchestration and similar technologies.

• Experience designing and operating cloud-native data infrastructure using technologies such as AWS/GCP, infrastructure as code, containers, orchestration, and managed data services.

• Experience taking data or ML/AI systems into production, including reliability, observability, deployment, evaluation, and operational ownership.

• Practical experience with modern AI infrastructure, such as embeddings/vector retrieval, LLM evaluation and observability, or agentic workflows.