Staff Backend Engineer, Dubbing

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. ABOUT THE ROLE You will work on the engineering systems powering Synthesia's dubbing product, the multi-step pipeline that transforms existing videos into new-language versions while preserving lip sync, voice quality, timing, and overall video integrity. Your role centers on the core challenge: building a production system that orchestrates complex, long-running jobs (often taking tens of minutes to hours) with reliability, observability, and quality at every stage. You'll ensure that localized videos are indistinguishable from originals, working across transcription, speaker identification, translation, voice synthesis, and video rendering. You will be responsible for designing and evolving systems that handle: - End-to-end pipeline orchestration for long-running, multi-stage jobs - Quality layers across transcription accuracy, speaker diarization, lip-sync rendering, translation, voice cloning, and TTS - Integration of ML-driven components (providers and open-source models) into production workflows - Video and audio complexity (normalization, chunking, encoding, vocal separation, retiming) - Evaluation frameworks that prove measurable improvements in output quality You will own projects that span multiple systems and domains, such as: - Building robustness layers (retries, idempotency, failure recovery) for long-running pipelines - Designing persistence and state management to ensure consistent voice outputs across regenerations - Improving how video and audio data is processed, cached, and reused - Integrating new transcription, translation, voice synthesis, and video rendering providers - Building evaluation harnesses around each pipeline stage to measure quality reliably You will evaluate your work through system performance, user experience metrics, and observability, using tracing and debugging tools to identify bottlenecks and continuously improve reliability. You will collaborate closely with product, frontend, and ML/R&D teams, ensuring backend systems support both current product needs and future innovation in video localization. WHAT WE'RE LOOKING FOR Must-haves - Strong production backend engineering fundamentals (design, reliability, performance, maintainability) - Experience building and operating async, batch, or long-running workflow systems (jobs, retries, failure modes, observability) - Comfort operating in ambiguity and making trade-off decisions (quality vs cost vs speed) - Enough ML literacy to integrate, evaluate, and iterate on models and third-party providers (not necessarily an MLE) - A product mindset focused on solving user-facing problems from a backend perspective Nice-to-haves - Video, audio, or media pipeline experience (codec, fps, ffmpeg-like realities) - Shipped systems that integrate ML outputs into product-facing workflows - Built evaluation frameworks for quality (both offline testing and production monitoring) - Experience with observability tools (e.g., Datadog), workflow systems (e.g., Temporal), or recommendation/evaluation systems - Willingness to step outside your comfort zone—including jumping into frontend code to debug end-to-end flows Why join us? We’re living the golden age of AI. The next decade will yield the next iconic