Note: The job is a remote job and is open to candidates in USA. Change.org is the world’s largest platform for democracy, using technology and AI to give people greater voice and drive social impact. The Staff Engineer, Data and AI Enablement will build and scale the data and AI platform, pipelines, architecture, and tooling that support trusted insights, personalization, experimentation, machine learning, and AI-enabled experiences. The role also provides technical leadership, operational ownership, mentoring, and architectural guidance across the organization.
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
- This job is expected to participate in our on call rotation
Skills
- 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
- 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
Company Overview