Staff Research Scientist- Interactive Avatars

Wer wir sind

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.

What you'll do

Set the research direction and roadmap for dyadic interaction modeling, balancing long-term bets with near-term product impact. Advance the state of the art in the perceptual layer of interactive agents, including understanding user audio and video and generating contextually appropriate reactions. Post-train multimodal models to generate rich, natural dyadic interactions from user audio and video inputs. Adapt diffusion models to new conditioning signals, such as conversational state, turn-taking, and listener cues. Build robust evaluation frameworks and test suites for continuous tracking of interaction quality. Partner with our data team to define data needs and shape high-quality datasets. Lead and mentor a small group of researchers, and drive technical decisions across research, data, and engineering.

What we're looking for:

Deep ML expertise and extensive hands-on experience with diffusion models, ideally for video or avatar generation. A strong publication record at top-tier venues (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, SIGGRAPH) in areas such as world models, dyadic interaction, or video diffusion, or equivalent demonstrated impact. Experience leading a small team of researchers and mentoring junior members. A track record of taking research from idea to production. Proficiency in PyTorch and modern ML tooling for large-scale training. Clear communication of hypotheses, experiments, and results, and the ability to influence direction across teams. Experience with real-time or streaming generation, including autoregressive video diffusion. Distillation or other techniques for low-latency inference. Audio-driven facial, gesture, or full-body motion modeling. Conversational modeling, such as turn-taking, backchanneling, or listener-response generation.

Fähigkeiten

PyTorch

Gehaltsspanne

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