Synthesia's long-term vision is to build the best human-interactive models: systems that don't just talk to people, but perceive and respond to them, reacting to a user's actions and emotions, not only their words. Today over 60,000 businesses rely on our platform, and the next leap in what we can offer them depends on models that combine text, audio, and video into a single real-time interactive experience
As a Staff Research Engineer, you'll join the Voice team within our 40+ person R&D department, but your scope will extend well beyond voice. You'll help define and drive that broader vision across teams, proposing ambitious research directions and taking direct ownership of the design and implementation of its most critical components. You'll work directly with our voice lead and collaborate tightly with our video teams and other senior members of the org
Concretely, you'll work on voice to voice models that produce text and voice simultaneously. Models that can reason, interrupt the user with back channeling and talk. Models that feel like you are having a natural conversation with, without the feeling of turn taking that is dominant in current speech to speech models. Your role would be to partner in defining a roadmap, implementing it and shipping the outcomes to product
Shape our roadmap to create new model capabilities and unlock new functionality for our customer base, on both short and long time horizons
Propose novel multi-modal system architectures (especially text and voice)
Develop and evaluate streaming and conversational systems for low-latency, interactive voice-video synthesis
Design solutions that reinforce emotional expressiveness and natural interaction
Implement and bring designs to life, from pretraining through post-training
Integrate and test novel architectures (neural codecs, diffusion, flow-matching) to enhance realism and responsiveness
Define new evaluation metrics for conversational systems, including latency-aware and interaction-based measurements
Track the latest research in audio-visual diffusion, autoregressive models, neural codecs, and multimodal LLMs
Curate new datasets to complement existing data
Lead post-training initiatives like DPO, fine-tuning, and distillation to bring models to shipping quality
Ship models to production with optimised runtime to serve customers, and address their feedback thereafter
Having shipped a generative model into a live product used at meaningful scale, not just published or prototyped it
Working on conversational or interactive systems where latency, responsiveness, and user experience were first-class constraints, not afterthoughts
Working on LLMs with large scale trainings leadings to models with decent reasoning capabilities
Owning a research problem end to end: from architecture proposal through pretraining, post-training, and production deployment
Collaborating across modalities or teams (e.g. audio and video, or research and product) to ship a unified system