Top Engineering Jobs in San Francisco, CA

As QA Engineering Manager, you will define QA strategies, manage a QA team, and integrate testing into product release cycles while ensuring compliance and quality standards are met.

As a Solutions Architect at Deepgram, you will engage with customers to resolve technical challenges, build scalable solutions, and enhance support infrastructure. The role involves post-sales engagement, automation development, and collaboration with teams to improve customer experience and advocate for their needs.

Design, develop, and maintain scalable backend systems for automated order-taking platforms, collaborating with teams to integrate AI models and optimize performance across noisy environments.

As a Software Engineer at Deepgram for Restaurants, you'll design and maintain scalable backend systems for automated ordering, integrating AI models, optimizing performance, and ensuring smooth interactions with restaurant software.

Lead engineering teams to design and implement core voice AI APIs. Responsible for team management, project planning, technical strategy, and collaboration with research and product teams.

As Model Evaluation QA Lead, you'll ensure model quality assurance across Deepgram's AI pipeline, designing automated evaluation frameworks, integrating quality gates into release pipelines, and ensuring high standards for model deployments.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

As a Network Engineer at Deepgram, you'll optimize global network infrastructure for real-time audio streaming, focusing on latency reduction across hybrid cloud and on-premises environments.

Design and implement secure, scalable backend services for voice agents, integrate AI models and telephony, improve inference, debug concurrent systems, and collaborate with Product to deliver features end-to-end.