Staff Research Engineer - Multimodal Generative Modelling

What you'll do 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. You'll thrive in this role if you have The ability to bring novel ideas and designs that advance the field of interactive multimodal systems. Strong understanding of generative modelling, ideally applied to sequential or multimodal data. Hands-on experience with large language models or similar transformer-based architectures. High proficiency in PyTorch, including distributed training and model optimization. A solid grasp of time-series modeling and tokenization, preferably in the context of audio, speech, or video. A demonstrated ability to prototype quickly, test hypotheses, and iterate efficiently. Proven experience training deep learning models end-to-end, from data preparation through evaluation. Strong general software engineering skills, enabling contributions to a large, shared research infrastructure. Particularly relevant experience 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.

Bonus points for Experience with real-time or streaming architectures. Familiarity with state-of-the-art architectures in audio and speech generation, such as diffusion models, neural codecs, flow-matching models, or autoregressive decoders. Excellence in one or more of the following modalities: voice, text, video. Evidence of original research contributions, such as publications or open-source work at top-tier venues (e.g. NeurIPS, CVPR, ICML, ICLR, Interspeech).

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