We’re hiring a Founding ML Research Engineer to work work through the full stack across data, pre-training, post-training & evals for training Generalist Audio Models. You’ll work through the entire stack with small team, tons of compute, high autonom, and see your research ideas making it to production within a week(s).
**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.
**What we’re looking for**
* 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?
**To apply**\
Share arxiv links to papers that: you co-authored, you enjoyed reading, you found surprising.
Visa sponsorship is offered for this role.
