Staff Engineer, Data and AI

Change.org

1d • Remote

About The Position

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.

Requirements

• Distributed data systems expertise: Able to design and scale reliable batch and real-time data architectures.
• Strong software engineering judgment: Builds maintainable, testable production systems in Python and/or comparable languages.
• Data modeling and SQL expertise: Designs scalable, trustworthy data models and data products.
• Cloud and platform architecture: Makes sound trade-offs across compute, storage, orchestration, streaming, infrastructure, and cost.
• Operational excellence: Demonstrates strong operational ownership through observability, incident response, on-call participation, runbooks, performance tuning, and building resilient systems.
• AI engineering fluency and technical leadership: Understands modern AI and LLM infrastructure and leads through architecture, collaboration, mentoring, and influence.
• 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.
• Professional-level English proficiency is required for all roles. While we are a global company, we ask that all resumes and application responses be submitted in English.

Responsibilities

• Partner with PMs to translate business and product opportunities and our shared strategic vision into scalable data and AI solutions, from early exploration through production rollout.
• Deliver reliable data products that support data and AI enabled features, experimentation, personalization and decision-making across the company.
• Build and scale batch and real-time pipelines that ingest, transform, and prepare high-quality data for reporting, machine learning training, model evaluation, feature generation, and production inference.
• Evolve the data and ML platform architecture across orchestration, storage, compute, streaming, and data access, using technologies such as Airflow, Kafka, Redshift, Glue, Vector DBs and other cloud native services.
• Improve data trust and usability by establishing strong practices for data modeling, schema evolution, data contracts, testing, lineage, privacy controls, freshness, and recoverability.
• Enable teams to work more independently by creating reusable tools, standards, and paved paths that make it easier to discover data and build dependable workflows.
• Maintain a resilient and efficient platform through observability, alerting, runbooks, incident response, on-call participation, performance tuning, and ongoing cost optimization.
• Raise the technical bar for data and AI infrastructure by leading architectural decisions, mentoring engineers, reviewing designs and code, reducing technical debt, and advancing the use of AI and agentic workflows.
• Participate in our on call rotation.

Benefits

• Compensation philosophy is based on pay equity.
• Salaries are determined before roles are launched.
• Salaries are based on a predetermined salary scale, the level on that scale and the cost of labor for that location.
• Benefits and perks also vary based on location.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed