Top Tech Jobs & Startup Jobs in Chicago, IL

Build and operate petabyte-scale audio data pipelines for AI model training. The role involves discovering data sources, developing large-scale processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. Candidates will own engineering projects end to end and should have strong backend software engineering experience, Python and Bash proficiency, and production systems expertise. Experience with web crawlers, distributed processing, cloud infrastructure, Terraform, machine learning datasets, or audio processing is advantageous.

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, ambiguous environment.

Build scalable, reliable data pipelines for AI model training, including discovering audio data sources, optimizing large-scale processing for quality, throughput, and cost, and collaborating with AI scientists on training datasets. The role owns engineering projects from discovery through continuous improvement and may involve web crawling, distributed processing, cloud infrastructure, and Infrastructure as Code.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio sources, improving data-processing systems for quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects end to end. The role requires strong backend software engineering, production systems experience, Python and Bash proficiency, and independent problem-solving. Preferred experience includes web crawling, data acquisition, distributed processing, GCP, Terraform, ML datasets, and audio processing.

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.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with emphasis on backend development, production systems, data acquisition, and scalable infrastructure.

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 data sources, developing reliable ingestion pipelines, managing engineering projects end to end, and supporting next-generation AI products.

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 training datasets and next-generation models while owning engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, production systems experience, and the ability to work independently in an ambiguous environment.

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 while working independently in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. The role involves discovering data sources, improving data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists on training datasets. Candidates will own engineering projects end to end and work with Python, Bash, Linux, cloud infrastructure, and potentially GCP and Terraform in a fully distributed environment.