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Build and operate large-scale data pipelines that source, process, and improve audio datasets for AI model training. The role focuses on data quality, throughput, cost optimization, production systems, and collaboration with AI scientists. Responsibilities include discovering audio sources, implementing scalable processing systems, owning engineering projects, and continuously improving training-data infrastructure.

Build scalable, cost-efficient data pipelines and processing systems for AI model training. Responsibilities include discovering audio data sources, creating reliable ingestion pipelines, optimizing data quality and throughput, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, Python and Bash proficiency, and independent problem-solving.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. Own engineering projects from discovery through continuous improvement. Requires strong backend software engineering, Python and Bash proficiency, production systems experience, and independent problem-solving.

Build and operate large-scale data pipelines that acquire, process, and improve audio datasets for AI model training. The role involves optimizing data quality, throughput, and cost; collaborating with AI scientists; exploring data sources; and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, and proficiency in Python, Bash, and Linux, with preferred experience in web crawling, distributed processing, cloud infrastructure, Terraform, ML datasets, or audio processing.

Build and operate large-scale audio data pipelines for AI model training, including discovering data sources, optimizing processing systems for quality, throughput, and cost, and collaborating with AI scientists. The role requires backend software engineering, production systems experience, and independent ownership of projects from discovery through continuous improvement.

Build and operate large-scale data pipelines that create high-quality audio datasets for AI model training. Responsibilities include discovering audio data sources, optimizing data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale data pipelines for AI model training, including discovering audio data sources, processing datasets, and optimizing quality, throughput, and cost. Collaborate with AI scientists on training data and own engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, Python, Bash, and Linux proficiency.

Build and operate large-scale data pipelines that collect, process, and improve audio datasets for AI model training. Responsibilities include discovering data sources, optimizing data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering experience, Python and Bash proficiency, Linux expertise, and production systems experience.

Build and operate large-scale AI training data pipelines, discover audio data sources, optimize data processing for quality, throughput, and cost, and collaborate with AI scientists on next-generation models. The role owns engineering projects from discovery through implementation and continuous improvement in a fully distributed environment.