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Staff Research Engineer - Video Post Training

Synthesia · Europe

Remote Staff
QuantizationDPODistillationDeep learningComputer visionCI/CDPythonGoPyTorchGPU

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 As a Staff Research Engineer you will join a team of 40+ Researchers and Engineers within the R&D Department working on cutting edge challenges in the Generative AI space, with a focus on creating highly realistic, emotional and life-like Synthetic humans through text-to-video. Within the team you’ll have the opportunity to work on the applied side of our research efforts and directly impact our solutions that are used worldwide by over 55,000 businesses. If you are an expert in post-training Diffusion models for generative AI, this is your chance. This is an opportunity to work for a company that is impacting businesses at a rapid pace across the globe. In this position, you’ll join our Post-Training Team that works on generative video creation. You will make it possible to apply our foundational models to production use cases. This includes projects such as: - Apply DPO (direct preference optimisation) to pre-trained models. - Adapt models to extend their capabilities, for instance, by changing conditioning inputs. - Build solutions for dubbing and evaluate the quality of lip-sync. - Implement post-training optimization techniques, such as quantization, pruning and distillation,

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