murmuration

Research Engineer

• Work across the stack: You have strong depth in one or more critical areas (e.g., API design, frontend architecture, data integration, infrastructure) and broad proficiency across the rest. You'll use whatever it takes — full-stack applications, backend systems, data pipelines, AI integrations — and continue deepening your expertise over time.
• Build end-to-end systems from data infrastructure that processes large-scale datasets reliably and cost-effectively to intuitive interfaces that make sophisticated analysis accessible to non-technical users like journalists, organizers, and decision-makers. You’ll turn manual, time-intensive processes into fast, scalable, automated ones.
• Apply emerging technologies (including AI/ML) to R&D challenges. You’ll prototype novel approaches, evaluate feasibility, and bridge the gap between cutting-edge capabilities and practical usability.
• Communicate clearly: You’ll provide actionable guidance to research and data science stakeholders on technical feasibility, solution alternatives, and tradeoffs. produce clear technical documentation (e.g., design docs, architecture decisions, and runbooks), and build consensus on design approaches across the team.
• Elevate the team: Mentor engineers at all levels through pairing, code review, and technical guidance, set high standards for code quality and testing that raise the bar for the entire team, and drive process improvements, better tooling, and shared knowledge that make the team more effective over time.

Senior Research Engineer

QA Engineer

• Help define and drive Murmuration’s QA strategy across multiple engineering pods, ensuring consistent quality practices and standards;
• Partner with our QA group to help establish and maintain QA processes, including test planning, automation strategy, release readiness, and defect management;
• Identify gaps in quality processes and lead cross-team initiatives to improve reliability, test coverage, and engineering efficiency;
• Serve as a subject-matter expert on QA methodologies, tooling, and best practices;
• Design, build, and maintain automated test suites for web applications, APIs, backend services, and data pipelines;
• Develop automated data validation tests for large-scale datasets, ETL workflows, and analytics systems (i.e., Python, Pandas, SQL, and NoSQL);
• Implement and maintain CI/CD-integrated test automation frameworks;
• Contribute to performance, load, and reliability testing for critical systems;
• Perform manual testing when appropriate, especially for exploratory, usability, or high-risk areas;
• Work closely with developers, product managers, designers, and data engineers to ensure high-quality delivery of features and platform improvements;
• Participate in design reviews, requirement refinement, and architecture discussions to ensure testability and quality considerations are built in from the start;
• Collaborate with DevOps and platform teams to improve deployment pipelines, observability, and release confidence;
• Mentor other QA engineers and developers on testing strategies, automation techniques, and quality best practices;
• Promote a culture of quality across engineering through documentation, training, and process improvements;
• Advocate for inclusive, collaborative, and data-driven engineering practices.

Senior QA Engineer

Data Engineer

• Own data pipelines and infrastructure: Design, implement, and evolve scalable, production-grade systems using tools such as Dagster, Airflow, Snowflake, AWS, MongoDB, and dbt. Apply a cloud-native and DevOps mindset using CI/CD, infrastructure-as-code, monitoring, and automated testing to build reliable systems. Partner with cross-functional teams to deliver solutions that meet both immediate product needs and long-term organizational strategy.
• Lead data ingestion and integration: Bring in complex, high-volume datasets while ensuring strong data contracts, freshness, quality, integrity, and lineage, and build systems that empower domain experts to contribute to and maintain their own data pipelines.
• Transform raw data into trusted data products: Convert raw inputs into structured, usable datasets that empower our analytical and product teams. Collaborate closely with operational data managers to ensure data models and intuitive, reliable alignment with how data is consumed in practice.
• Leverage AI: Make informed judgement calls about how AI can be a force-multiplier for both your own work and the team’s and how it can’t.
• Elevate the team: Mentor engineers, actively shape technical direction through architectural reviews and roadmap planning, and build team culture through documentation and knowledge sharing.

Senior Research Engineer

Senior QA Engineer

Senior Data Engineer