Research Engineer

San Franciscofull timeOn Site

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mid

$210k–$360k

H1B: no

David AI Research Engineer at David AI builds algorithms and systems to convert large-scale speech and audio data into high-quality datasets. The role involves designing signal processing algorithms, building scalable data pipelines, and deploying ML inference systems.

Responsibilities

As a Research Engineer at David AI, you'll build algorithms and systems that turn large-scale speech and audio collections into high-quality datasets. You'll tackle questions like: What makes a recording useful? How do we measure and improve dataset quality?
Design algorithms and metrics using advanced signal processing algorithms and cutting edge ML models with to derive insights from thousands of hours of audio data.
Build scalable data processing pipelines for analyzing, filtering, deduplicating, and curating large speech and audio datasets.
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.

Qualification

End-to-end ownership of ML pipelinesExperience with speechExperience with data curationExperience in releasing

Required

3+ years of professional engineering or applied research experience in areas such as data systems, algorithms, applied ML, information retrieval, or speech and audio.
Strong Python skills and experience building maintainable, production-quality software.
End-to-end ownership of ML pipelines, from proof-of-concept to production deployment.
Ability to translate research papers and ideas into high-quality, production-ready code.
Experience deploying ML systems for production inference with cloud technologies.
Ability to assess model quality in the context of user experience and business value.

Preferred

Experience with speech, audio, or digital signal processing.
Experience with data curation, search and retrieval, embeddings, clustering, deduplication, or sampling.
Experience building model inference systems or integrating pretrained models into production workflows.
Experience in publishing in top conferences such as NeurIPS, ICML, ICLR, CVPR, etc.
Experience in releasing, maintaining, contributing to open-source softwares.
PhD or Masters in Computer Science or a related field.