Top Tech Jobs & Startup Jobs in Denver & Boulder, CO

As a Partner Success Engineer at Deepgram, you will drive partner engagement, manage technical enablement, and ensure successful integration of AI solutions, while continuously optimizing workflows and fostering collaboration across teams.

Lead design and delivery of an agent-based Intelligence Layer that autonomously analyzes product, usage, and revenue data. Build AI-augmented workflows, define semantic/data contracts, leverage voice models to extract insights from audio, advise CEO/Board on unit economics and NRR, and hire a small elite analytics engineering team.

The role involves owning the go-to-market strategy for TTS, defining its narrative, leading product launches, enabling sales, and collaborating across teams.

The Engineering Manager will lead a team developing web applications and AI tools, optimizing product-led growth strategies and enhancing the developer experience.

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 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.

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 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.

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.

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