Machine Learning Research Scientist

At Phonic, we are building a platform to help users build, observable, and evaluate voice apps with a focus on making them reliable. Phonic helps the user increase reliability of voice agents while offering lower cost and latencies. 

To do so, we are training audio foundation models from scratch on petabytes of data that speak with life-like conversationalness at extremely fast latencies. We are a team of experts from MIT and Stanford working on cutting-edge model research and ML infrastructure scaling challenges to unlock new generative capabilities that are a step beyond what is possible today. We've raised a seed round from Lux Capital and are looking for a Machine Learning Research Engineer to join our office in San Francisco in our mission to reinvent the future of audio generation.

 Some potential areas that you could work on:

• Model Training: how can we train models that help our users describe audio? How should we allocate FLOPs to model parameters versus training data?
• Data: how can we generate training data at a reasonable cost? How can we design algorithms that help filter our low-quality data from the high-quality data?
• RLHF: we have preference data from our application, how can we help feed that into our model to discourage certain outputs?

*There are no hard requirements, as long as you can demonstrate you are able to write a lot of good code*

• Previous experience working with big data pipelines and infrastructure to support large-scale model training.
• Ability to learn and iterate quickly.
• Self-motivated with a willingness to take ownership of tasks.
• Take pride in building and operating scalable, reliable, secure systems.
• Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done.