Staff Engineer, Data and AI

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]

Change.org http://Change.org is searching for a Staff Engineer, Data and AI Enablement to build and scale the data and AI platform behind Change.org http://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 http://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.

Key Outcomes:

• 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.

• This job is expected to participate in our on call rotation

The most important core competencies for the role are:

• 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.

Target experience:

• 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.

Interested? Great! Here's what you should know:

This is a full time role based in the United States, Canada or Mexico.

We’re currently able to hire staff based in the following US locations: Alaska (AK), Arizona (AZ), California (CA), Colorado (CO), Connecticut (CT), District of Columbia (DC), Florida (FL), Georgia (GA), Illinois (IL), Iowa (IA), Kansas (KS), Maryland (MD), Massachusetts (MA), Michigan (MI), Minnesota (MN), Missouri (MO), New Jersey (NJ), New York (NY), North Carolina (NC), Oregon (OR), Pennsylvania (PA), Rhode Island (RI), Texas (TX), Vermont (VT), Virginia (VA), Washington (WA), Wisconsin (WI).

We're also able to hire staff in Canada (AB, BC, ON) and Mexico.

Our compensation philosophy is based on pay equity. All of our salaries are determined before we launch a role – they are based on a predetermined salary scale, the level on that scale and the cost of labor for that location.

The annual salary of a Staff Engineer, Data and AI is 236,000 in San Francisco and New York, and is 224,000 in Austin, Boston, Chicago, DC, LA and Seattle and is $200,500 in all other US locations.

The annual salary of a Staff Engineer, Data and AI is 221,500 CAD in Vancouver and Toronto, Canada, and is 205,500 CAD in Victoria and Calgary and is $202,000 CAD in all other Canadian locations.

The annual salary of a Staff Engineer, Data and AI is $1,727,000 MXN anywhere within Mexico.

Benefits and perks also vary based on location.

Our evaluation process is as follows:

• Recruiter Screen

• Hiring Manager Screen

• Technical Assessment

• Team Interview

• Exec Interview

We recognize that people assess their qualifications differently, and you may not meet every requirement listed. If you believe you could be successful in this role, we encourage you to apply.

Change.org http://Change.org is an open platform designed to serve people across the full range of viewpoints. Effectively supporting that mission requires teams with a wide variety of experiences, skills, and perspectives, which we consider essential to our work.

We are an equal opportunity employer and consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity [or expression], national origin, protected veteran status, disability, age, or any other status protected by law.

We're committed to protecting your data. To learn more, please review our Change.org http://Change.org Job Applicant Privacy Policy. https://www.change.org/policies/applicants

We are committed to providing reasonable accommodations for candidates with disabilities throughout our recruitment process. If you need assistance or an accommodation, please let your recruiter know.

Change.org http://Change.org voluntarily collects limited demographic information for government reporting and to support our diversity and inclusion efforts. Providing this information is entirely optional and anonymous, and is not used in hiring decisions.

The categories in this survey are standardized and may not reflect how everyone describes themselves.

Change.org http://Change.org participates in E-verify https://www.e-verify.gov/sites/default/files/everify/posters/EVerifyParticipationPoster.pdf - click here https://www.e-verify.gov/sites/default/files/everify/posters/IER_RightToWorkPoster%20Eng_Es.pdf to learn more.

#LI-Remote

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.