Top Tech Jobs & Startup Jobs in Boston, MA

Build scalable data pipelines and processing systems for AI model training datasets. 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, operating production systems in ambiguous environments.

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 on training datasets. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed environment.

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

Build and operate petabyte-scale audio data pipelines for AI model training. The role involves discovering data sources, improving large-scale processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The engineer will own projects from discovery through implementation and continuous improvement, using Python, Bash, Linux, cloud infrastructure, and potentially GCP and Terraform.

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

Build and operate large-scale pipelines for acquiring, processing, and improving audio datasets used to train AI models. The role involves optimizing data quality, throughput, and cost; exploring new data sources; collaborating with AI scientists; and owning engineering projects from discovery through continuous improvement. Strong backend software engineering, Python, Bash, Linux, production systems, and data-processing experience are required.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering 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 skills, production-systems experience, and proficiency in Python and Bash in Linux environments.

Build scalable AI training data pipelines, discover and acquire audio data, and optimize large-scale processing systems for quality, throughput, and cost. Collaborate with AI scientists on datasets and next-generation models while owning engineering projects from discovery through continuous improvement. The role requires backend software engineering, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale data pipelines that produce high-quality audio datasets for AI model training. Responsibilities include discovering audio data sources, developing 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, and scalable data processing.

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