data
Posted 4 weeks agoSenior Machine Learning Research Engineer
at David AI
San Francisco, United StatesOn-site
Responsibilities
- - Develop production-grade inference algorithms, pipelines, and APIs with cross-functional teams that unlock key insights into our data for our customers.
- - Architect systems that enable resilient, durable inference and evaluations.
Requirements
- ABOUT DAVID AI David AI is the first audio data research company.
- We bring an R&D approach to data–developing datasets with the same rigor AI labs bring to models.
- Our mission is to bring AI into the real world, and we believe audio is the gateway.
- As audio AI advances and new use cases emerge, high-quality training data is the bottleneck.
- This is where David AI comes in.
- David AI was founded in 2024 by a team of former Scale AI engineers and operators.
- In less than a year, we’ve brought on most FAANG companies and AI labs as customers.
- We’re looking for the best research, engineering, product, and operations minds to join us on our mission to push the frontier of audio AI.
- ABOUT OUR MACHINE LEARNING TEAM Our Machine Learning team sits at the intersection of cutting-edge research and production systems, transforming raw audio into high-signal data for leading AI labs and enterprises.
- We own the full ML lifecycle - from researching novel speech processing algorithms to deploying models processing terabytes of audio daily.
- ABOUT THIS ROLE As a ML Research 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.
- algorithms and cutting edge ML models with application to speech and audio.
- YOUR BACKGROUND LOOKS LIKE - 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.