Top Tech Jobs & Startup Jobs in Seattle, WA

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

Build scalable, reliable pipelines for acquiring and processing large-scale audio datasets used to train AI models. Improve data quality, throughput, and cost; collaborate with AI scientists; explore new data sources; and own engineering projects from discovery through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable ingestion and 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 data pipelines and processing systems for AI model training. The role involves discovering audio data sources, optimizing data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. Candidates should have strong backend software engineering experience, production systems expertise, and proficiency in Python, Bash, and Linux. Experience with web crawlers, distributed processing, cloud infrastructure, Terraform, ML datasets, or audio processing is advantageous.

Build scalable AI training data pipelines, discover and acquire audio data, optimize large-scale data processing for quality, throughput, and cost, and collaborate with AI scientists on next-generation models. The role requires production backend engineering, independent project ownership, and experience with data pipelines or distributed processing. Preferred experience includes web crawling, cloud infrastructure, Terraform, machine-learning datasets, and audio processing.

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 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 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.