Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

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. 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 AI training-data pipelines and large-scale data-processing systems. Discover and acquire audio data, optimize 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 scalable data pipelines and large-scale processing systems for AI model training. Discover audio data sources, improve dataset quality, optimize throughput and cost, collaborate with AI scientists, and own engineering projects from discovery through continuous improvement.

Build scalable data pipelines and processing systems for AI model training datasets. Discover audio data sources, optimize data quality, throughput, and cost, and collaborate with AI scientists on next-generation models. The role requires ownership of engineering projects, production backend development, and independent problem-solving in ambiguous environments.

Build and operate large-scale data pipelines that source, process, and optimize audio datasets for AI model training. Responsibilities include discovering data sources, improving data quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects end to end. The role requires strong backend software engineering, Python and Bash proficiency, production systems experience, and the ability to work independently in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering audio data sources, developing reliable ingestion and processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists on training datasets. The role requires ownership of engineering projects, strong backend development, production systems experience, and independent problem-solving in an ambiguous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, optimizing 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 data pipelines for AI model training, including discovering audio sources, improving data-processing systems, and optimizing 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, Python, Bash, and Linux proficiency.

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, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement. The role requires strong backend engineering, production systems experience, and independent problem-solving in a distributed environment.

Build and operate large-scale AI data pipelines, discover audio data sources, optimize data-processing systems for quality, throughput, and cost, and collaborate with AI scientists on training datasets. The role requires backend software engineering, production systems experience, independent problem-solving, and ownership of projects from discovery through continuous improvement. Nice-to-have experience includes web crawling, distributed processing, cloud infrastructure, Terraform, dataset quality, and audio processing.