Top Tech Jobs & Startup Jobs in Boston, MA

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.

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

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

Build scalable pipelines and data-processing systems for AI model training. Responsibilities include discovering audio data sources, improving dataset quality, optimizing 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 with Python, Bash, and Linux. Experience with web crawlers, distributed processing, GCP, Terraform, and audio or ML training data is advantageous.

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 through continuous improvement.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role owns engineering projects end to end and requires strong backend software engineering skills, Python and Bash proficiency, production systems experience, and independent problem-solving.

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. Responsibilities span engineering projects from discovery through continuous improvement, with emphasis on production systems, distributed processing, and scalable dataset development.

Build and operate large-scale data pipelines that acquire, process, and optimize audio datasets for AI model training. Responsibilities include discovering data sources, improving data quality and processing efficiency, collaborating with AI scientists, and owning engineering projects through continuous improvement. The role requires strong backend software engineering, production systems experience, and proficiency with Python and Bash in Linux environments.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable 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.