Jobleads-US
Member of Technical Staff - Audio
Jobleads-US - Palo Alto, California, United States, 94306
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Overview
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Overview
About this role
You will be a core contributor on Zyphra’s Audio Team building the next generation of open-source text-to-speech and audio models. You will be deeply involved in the entire model training process from data gathering and processing to designing novel architectures and training methodologies. You will work across: Large-scale audio training runs
Performance optimization of our training stack
Audio dataset collection, processing, and evaluation
Architecture and training methodology ablations and improvements
While deep expertise in all areas isn't required, you should be enthusiastic about learning each aspect as needed to drive our models forward. We also strongly look for velocity over position. However, having deep expertise in at least one of the above is helpful to begin contributing immediately. Requirements
Strong research taste and intuition. The ability to work through a research project from conception to execution to write-up.
Strong implementation and prototyping ability – can take an idea from conception to experimentation quickly
The ability to work well with others in a high-paced research setting
Can rapidly learn new fields and are excited to implement new ideas
Excellent communication and collaboration skills and can work effectively on both research and engineering implementation at scale.
Good to have
Expertise and intuition for training models in the audio domain including text-to-speech, ASR, speech-to-speech, speech-emotion-recognition, or other models
Experience in training audio autoencoders.
Understanding of signal processing especially of audio signals.
Experience with diffusion models, consistency models, or GANs
Experience with training on large scale (multi-node) GPU clusters
Strong grasp of proper experimental methodology for running rigorous ablations and other hypothesis testing
Understanding of and interest in large-scale highly parallel data processing pipelines.
Proficiency with Pytorch and Python.
Experience contributing to large pre-existing codebases and rapidly getting up to speed.
Previously published machine learning research in well-respected venues.
Postgraduate degree in scientific subject (Computer Science, EE/EECS, Mathematics, Physics, Machine Learning)
Culture
Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued
We strongly value new and crazy ideas and are very willing to bet big on new ideas
We move as quickly as we can; we aim to make the bar to impact as low as possible
We all enjoy what we do and love discussing AI
Benefits and Perks
Medical, dental, vision and FSA plans
Competitive salary, equity and 401(k)
Relocation and immigration support on a case-by-case basis
On-site meals prepared by a dedicated culinary team; Thursday Happy Hours
General requirements
Willing to be in-person in our office in Palo Alto
US authorization to work. We will consider O1 visa sponsorship for the right candidate
#J-18808-Ljbffr
You will be a core contributor on Zyphra’s Audio Team building the next generation of open-source text-to-speech and audio models. You will be deeply involved in the entire model training process from data gathering and processing to designing novel architectures and training methodologies. You will work across: Large-scale audio training runs
Performance optimization of our training stack
Audio dataset collection, processing, and evaluation
Architecture and training methodology ablations and improvements
While deep expertise in all areas isn't required, you should be enthusiastic about learning each aspect as needed to drive our models forward. We also strongly look for velocity over position. However, having deep expertise in at least one of the above is helpful to begin contributing immediately. Requirements
Strong research taste and intuition. The ability to work through a research project from conception to execution to write-up.
Strong implementation and prototyping ability – can take an idea from conception to experimentation quickly
The ability to work well with others in a high-paced research setting
Can rapidly learn new fields and are excited to implement new ideas
Excellent communication and collaboration skills and can work effectively on both research and engineering implementation at scale.
Good to have
Expertise and intuition for training models in the audio domain including text-to-speech, ASR, speech-to-speech, speech-emotion-recognition, or other models
Experience in training audio autoencoders.
Understanding of signal processing especially of audio signals.
Experience with diffusion models, consistency models, or GANs
Experience with training on large scale (multi-node) GPU clusters
Strong grasp of proper experimental methodology for running rigorous ablations and other hypothesis testing
Understanding of and interest in large-scale highly parallel data processing pipelines.
Proficiency with Pytorch and Python.
Experience contributing to large pre-existing codebases and rapidly getting up to speed.
Previously published machine learning research in well-respected venues.
Postgraduate degree in scientific subject (Computer Science, EE/EECS, Mathematics, Physics, Machine Learning)
Culture
Our research methodology is to make grounded, methodical steps toward ambitious goals. Both deep research and engineering excellence are equally valued
We strongly value new and crazy ideas and are very willing to bet big on new ideas
We move as quickly as we can; we aim to make the bar to impact as low as possible
We all enjoy what we do and love discussing AI
Benefits and Perks
Medical, dental, vision and FSA plans
Competitive salary, equity and 401(k)
Relocation and immigration support on a case-by-case basis
On-site meals prepared by a dedicated culinary team; Thursday Happy Hours
General requirements
Willing to be in-person in our office in Palo Alto
US authorization to work. We will consider O1 visa sponsorship for the right candidate
#J-18808-Ljbffr