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Designs, operates, and improves Duolingo’s large-scale distributed systems and core infrastructure. Responsibilities include diagnosing production issues, developing platforms and automation, conducting launch reviews and root cause analyses, maintaining incident response and postmortem practices, and improving reliability, scalability, and engineering velocity. The role partners with product and platform engineering teams to reduce operational toil and ensure high-quality service delivery.

The Senior Site Reliability Engineer will improve the reliability, scalability, and operational performance of Duolingo’s distributed systems. Responsibilities include diagnosing production infrastructure, designing systems, developing automation and platforms, conducting launch reviews and root cause analyses, maintaining incident response and postmortem practices, reducing operational toil, and supporting engineering teams during feature releases.

Lead the AI Tooling and Ecosystem Team in developing tools and services for datasets, prompts, evaluations, AI workflows, and model performance measurement. Manage engineers, build AI infrastructure and products, establish human and LLM-as-judge evaluation processes, streamline AI development, and partner with AI, product, data science, and platform leaders. Own multi-quarter roadmaps, cross-functional alignment, and initiatives that improve AI feature velocity and product impact.

Lead the AI Tooling and Ecosystem Team in building tools and services for datasets, prompts, model evaluations, human and LLM feedback, and AI workflows. Manage engineers, hire and develop talent, own multi-quarter roadmaps, and align AI, product, data science, and platform teams. Improve AI feature velocity, reliability, measurability, and cost efficiency through internal tools, APIs, evaluation systems, and human-in-the-loop pipelines.

Drive global mobile user acquisition campaigns across Google UAC, Meta, TikTok, and programmatic channels. Own campaign execution, creative strategy, budget optimization, agency partner management, and performance improvement across CPI, retention, LTV, CAC, and monetization metrics. Use attribution and marketing analytics to guide experimentation, develop growth opportunities, collaborate with cross-functional teams, and present strategic recommendations to marketing leadership.

Leads mixed-method product research across discovery, concept testing, and post-launch improvements for Duolingo’s global learner experience. Partners with designers and product managers to identify high-impact questions, guide product strategy, generate actionable insights, and establish research-sharing systems. Mentors researchers, improves research practices, and introduces methods and tools that increase rigor and speed.

Develop and improve trustworthy quarterly user-growth forecasts at Duolingo. Collaborate with Product, Finance, Strategy, leadership, and other data scientists to translate business and product changes into analytical requirements and actionable recommendations. Validate forecasting methodologies, build robust repeatable forecasting processes, communicate complex findings clearly, and mentor junior data scientists. The role requires strong quantitative expertise, real-world forecasting experience, and fluency in SQL, Python, and analytics engineering ecosystems.

Embed with product teams to use data science, machine learning, experimentation, and analytics to improve user growth. Build scalable data products, models, dashboards, and analytical foundations; prioritize opportunities and assess ROI; translate complex findings into actionable recommendations; collaborate with Product, Engineering, Design, Marketing, and Finance; and mentor other Data Scientists.

Develop and improve user growth forecasting methods, deliver reliable quarterly forecasts, and build robust forecasting processes. Collaborate with Product, Finance, Strategy, and senior leadership to translate business changes into analytical requirements and actionable recommendations. Communicate complex quantitative findings clearly, validate methodological advances, and mentor junior data scientists.

Embed with product teams to use data science, machine learning, experimentation, and analysis to improve user growth and product outcomes. Build scalable data products, models, dashboards, and analytical foundations; assess opportunities and ROI; communicate findings to leadership; collaborate with Product, Engineering, Design, Marketing, and Finance; and mentor other Data Scientists.