Deepfake detection and identity verification for voice and video channels
Pindrop builds real-time fraud, deepfake and identity-verification tooling for voice, video and digital interactions, running ML workloads on PyTorch, TensorFlow and SageMaker across both AWS and GCP. LangChain adoption alongside RAG and AI agents for incident analysis points to generative AI moving into internal engineering ops. Accelerating hiring splits between senior engineering and a finance build-out focused on forecasting, reporting automation and fundraising support.
Pindrop is an Atlanta-based security company founded in 2011 that detects deepfakes and verifies caller and user identity continuously across voice, video and digital channels. Its products target enterprises handling high-risk customer interactions, with call center authentication and fraud prevention as core use cases. The company is privately held with 201–500 employees and hires in the United States, Ukraine and Canada. Engineering runs on Go, Python, Java and C++ with Kubernetes, Terraform and streaming data services such as Kinesis and RabbitMQ.
Pindrop uses Go, Python, Java and C++ on AWS and GCP, with Kubernetes, Terraform, DynamoDB, PostgreSQL, Redis and Kinesis, plus PyTorch, TensorFlow and SageMaker for ML. It is adopting LangChain.
Pindrop hires in the United States, Ukraine and Canada, with hiring velocity accelerating and weighted toward senior engineering, finance and sales roles.
Current initiatives include migrating its Passport services to the cloud, standardizing cloud-native services, upgrading its observability stack, AI agents for incident analysis and code review, and AI-enabled reporting automation.
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Pindrop's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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