Location: San Francisco, CA, United States
Job Type: Full-time
Salary: $180K - $250K
Equity: 0.50% - 1.00%
Experience: 3+ years
Visa: Will sponsor
Skills: Machine Learning
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.
Summary
Research and build generalist audio models across data, pre-training, post-training and evals, including multi-modal architectures, codecs, SFT/RLHF-style training and scalable pipelines. Requires industry or academic experience pre-training or post-training large neural networks and strong ML systems engineering depth; speech or audio experience is a plus.
Responsibilities
Research multi-modal architectures and codecs; post-train audio models for instruction following and in-context learning over text and audio; build large-scale speech model pre-training and post-training; build scalable data and compute pipelines; review data and audio.
Qualifications
Industry or academic experience pre-training or post-training large neural networks; strong ML systems and engineering depth (distributed training, performance, reliability); comfort operating in ambiguity and shipping work.
Experience requirements
3+ years
How to apply
Share arxiv links to papers that: you co-authored, you enjoyed reading, you found surprising.