Staff Research Engineer - Multimodal Generative Modelling

• 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.

• 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.

• 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).