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Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

Build and scale Deepgram's federal partner network by owning strategy and execution for SI/prime and hyperscaler alliances. Drive sell-to/sell-through/sell-with motions, create joint solutions, enable partners at scale, instrument partner-sourced/influenced pipeline, and deploy AI-native automations to operate at scale. Represent Deepgram externally and coordinate cross-functionally to accelerate partner-driven revenue.

As a Billing & Analytics Software Engineer, you'll design and maintain the billing pipeline, integrate billing systems, and develop customer-facing analytics dashboards to support revenue recognition and operational observability.

As a Customer Success Engineer, you'll drive adoption and satisfaction for enterprise customers using Deepgram's voice AI technology, fostering strong relationships and aligning technical solutions with business objectives.

The Account Executive at Deepgram is responsible for building a sales pipeline, exceeding targets, collaborating with teams, and understanding customer needs to sell the voice AI platform.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

Lead the operational accounting function as Deepgram scales toward IPO, overseeing monthly close, ERP migration, and the development of accounting policies and controls.

Lead the edge deployment strategy for Deepgram's speech AI models, optimizing for performance on constrained hardware and collaborating with partners like Qualcomm to deliver secure solutions for defense customers.

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.

The role involves researching and developing large language models (LLMs) with a focus on transformer architecture, data curation, distributed training, and optimization. Responsibilities include conducting experiments, collaborating with teams, and staying updated on deep learning advancements.