Requirements

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.

Nice to Haves

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 leading 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.

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 optimized runtime to serve customers, and address their feedback thereafter.

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).