Otter.ai turns meeting conversations into searchable, agent-ready organizational knowledge. Its stack pairs PyTorch and JAX model work with RAG, Kafka, Cassandra and Elasticsearch, which points to in-house speech and retrieval systems rather than thin API wrappers. Senior-heavy engineering hiring, shared agent infrastructure projects and pain points around ML scaling and cost trade-offs show a push from notetaking toward enterprise AI agents.
Otter.ai builds an AI meeting assistant that transcribes conversations, captures action items and connects knowledge across an organization's meetings. The product runs on Mac and Windows desktop, iPhone, Android and the web, and it serves both individual users and enterprise teams. The privately held company is headquartered in Mountain View, California, and has 51–200 employees. Its go-to-market uses a sales stack built on Salesforce, Outreach and Sales Navigator.
Otter.ai runs on AWS with ECS and Kubernetes, managed with Terraform and Ansible. Its backend uses Python, Go, Java and Django, with MySQL, Redis, Cassandra, Kafka and Elasticsearch. ML work uses PyTorch, JAX and RAG.
Yes. Engineering is Otter.ai's largest hiring area, and most openings are senior or staff level. The company also hires in sales and marketing. All hiring is based in the United States.
Otter.ai is building shared agent infrastructure and AI-agent experiences for Otter AI Chat. Other projects include a single-page app transformation of its web application and CI/CD automation.
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Otter.ai'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 →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
