Telecommut
What You’ll Do
- Research better multi-modal architectures & codecs that are efficient across both spoken speech & general audio.
- Post-train audio models to have LLM like instruction following & in-context learning but over both text and audio.
- Build large-scale speech model pre-training and post-training (SFT/RLHF-style, distillation, preference optimization, etc.).
- Build scalable data + compute pipelines: dataset curation, filtering, mixing, tokenization/feature pipelines, evaluation harnesses.
- Look at lots of data & hear lots of audio.
- Industry/Academia experience pre-training / post-training large neural networks; speech/audio is a plus but not required, language/vision experience is also relevant.
- Strong ML systems and engineering depth (distributed training, performance, reliability).
- Comfort operating in ambiguity: you can spec, build, debug, and ship.
- A hunger to always ask - what would the next frontier look like?
Share arxiv links to papers that: you co-authored, you enjoyed reading, you found surprising.
