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Build and operate petabyte-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing large-scale processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed environment.

Build scalable, reliable pipelines for acquiring and processing large-scale audio datasets used in AI model training. Improve data quality, throughput, and cost while collaborating with AI scientists on next-generation models. Own engineering projects from discovery through implementation and continuous improvement, using backend development, production systems, and data-processing expertise.

Build scalable AI training-data pipelines and large-scale data-processing systems for high-quality audio datasets. Responsibilities include discovering audio 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, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists on next-generation models. Responsibilities include owning engineering projects from discovery through continuous improvement. Required experience includes backend software engineering, production systems, Python, Bash, and Linux; cloud infrastructure, Terraform, web crawling, and distributed processing are advantageous.

Build scalable data pipelines and processing systems for AI training datasets at petabyte scale. Discover audio data sources, improve data quality, throughput, and cost, and collaborate with AI scientists on next-generation models. Own engineering projects from discovery through continuous improvement in a distributed environment.

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, production systems experience, and proficiency with Python, Bash, and Linux.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio 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 and Bash in Linux environments.

Build and operate large-scale audio data pipelines for AI model training. Discover data sources, improve data-processing systems, and optimize dataset quality, throughput, and cost. Collaborate with AI scientists on next-generation models and own engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, Python and Bash proficiency, and the ability to work independently in an ambiguous environment.

Build and operate large-scale data pipelines that source, process, and improve audio datasets for AI model training. The role involves optimizing data quality, throughput, and cost; collaborating with AI scientists; exploring new data sources; and owning engineering projects from discovery through continuous improvement. Candidates need strong backend software engineering experience, Python and Bash proficiency, production systems experience, and the ability to work independently in ambiguous environments.

Build scalable data pipelines and processing systems for AI model training. Responsibilities include discovering audio data sources, optimizing dataset quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.