Applied Audio ML Engineer sf, ca
About The Position
As an Applied ML Engineer at David AI you'll build cutting-edge speech and audio models, production inference systems and resilient pipelines that showcase what high-quality data can really do. In this role, you will Research, design, and implement solutions using advanced signal processing. algorithms and bleeding edge ML models with application to speech and audio. Develop production-grade inference algorithms, pipelines, and APIs with cross-functional teams that unlock key insights into our data for our customers. Collaborating with our Operations team to gather useful training and evaluation datasets to improve the quality of our models. Architect systems that enable resilient, durable inference and evaluations.
Requirements
- 5+ years of professional audio ML experience, including DSP and ML audio algorithm development.
- End-to-end ownership of ML pipelines, from proof-of-concept to production deployment.
- Strong coding skills in Python and proficiency with deep learning frameworks such as PyTorch.
- Ability to translate research papers and ideas into high-quality, production-ready code.
- Experience deploying ML systems for production inference with cloud technologies.
- Track record of setting ML roadmaps, influencing technical direction, and prioritizing research and infrastructure investments.
- Ability to assess model quality in the context of user experience and business value.
Nice To Haves
- PhD or Masters in Computer Science or a related field.
- Experience training generative AI models.
- Expertise in audio signal processing both classical and machine learning techniques.
Responsibilities
- Research, design, and implement solutions using advanced signal processing algorithms and bleeding edge ML models with application to speech and audio.
- Develop production-grade inference algorithms, pipelines, and APIs with cross-functional teams that unlock key insights into our data for our customers.
- Collaborating with our Operations team to gather useful training and evaluation datasets to improve the quality of our models.
- Architect systems that enable resilient, durable inference and evaluations.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
