Top Tech Jobs & Startup Jobs in Charlotte, NC

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, and collaborating with AI scientists. The role owns engineering projects end to end and requires strong backend software engineering, production systems, and independent problem-solving skills.

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

Build and operate large-scale data pipelines for AI model training, including discovering audio sources, optimizing data processing for 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 and operate large-scale data pipelines for AI model training, including discovering audio data sources, optimizing data processing for quality, throughput, and cost, and collaborating with AI scientists. The role owns engineering projects from discovery through continuous improvement and requires strong backend software engineering skills, production systems experience, and proficiency with Python, Bash, and Linux. Experience with web crawling, distributed processing, GCP, Terraform, machine-learning datasets, and audio processing is beneficial.

Build scalable data pipelines and processing systems for AI model training. Discover audio data sources, optimize data quality, throughput, and cost, and collaborate with AI scientists on next-generation models. Own engineering projects from discovery through continuous improvement. The role requires strong backend software engineering skills, production systems experience, and the ability to work independently in ambiguous environments.

Build and operate large-scale data pipelines that collect, process, and optimize audio datasets for AI model training. The role involves discovering data sources, improving data quality, throughput, and cost efficiency, partnering with AI scientists, and owning engineering projects end to end. Strong backend software engineering, Python, Bash, Linux, production systems, and data-processing experience are required, with GCP, Terraform, web crawling, and audio or ML dataset familiarity preferred.

Build scalable AI training-data pipelines, discover audio data sources, and optimize large-scale data processing for quality, throughput, and cost. Collaborate with AI scientists on datasets and models, operate production systems, and own engineering projects from discovery through continuous improvement. The role requires strong backend software engineering, Python and Bash proficiency in Linux environments, and independent problem-solving. Experience with crawlers, distributed processing, GCP, Terraform, and audio or ML data is advantageous.

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.