Synthesia

Staff Backend Engineer role focused on the core systems powering Avatars and Generative Media for Synthesia's AI video platform. The role involves system design, architecture, scalability, reliability, and integration with voice and video pipelines, ensuring avatars function as a first-class entity. It requires a long-term view, technical stewardship, and collaboration with product, R&D, and adjacent teams.

Staff Fullstack Engineer role at Synthesia, an AI video platform company. The role focuses on building end-to-end product features in the Avatars and Generative Media domain, integrating frontend, backend, and AI model capabilities. Responsibilities include owning features from idea to production, working with both third-party and in-house AI models, and tackling 0 to 1 problems. Experience with AI/ML products, experimentation, or feature flagging is a plus.

Staff Software Engineer focused on the core systems for Synthesia's AI video platform, specifically script preview and voice generation. The role involves building and operating backend services that orchestrate multiple TTS providers and in-house models, ensuring reliability, consistency, and quality. Responsibilities include designing persistence systems, improving voice data handling, and working on voice discovery/recommendation features, including evaluation and observability systems. The role requires strong backend engineering skills, a product mindset, and collaboration with R&D and frontend teams.

Engineering Manager to lead the delivery of complex AI-powered video and generative media systems, focusing on avatar rendering and AI model integration. The role involves breaking down research initiatives, managing senior engineers, scaling the team, and ensuring system reliability and performance for an enterprise-grade SaaS product.

Commercial Data Scientist at Synthesia, an AI video platform company, responsible for building, deploying, and maintaining data science models to improve revenue outcomes and customer experience. Projects include customer health scores, lead intent scoring, churn/expansion predictors, segmentation, and experimentation frameworks. Requires end-to-end model lifecycle management, from problem definition to production deployment and maintenance, with a strong production mindset and collaboration with commercial stakeholders and Data Engineering.