Founding ML Research Engineer

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 autonomy, 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.