Senior Software Engineer - Applied AI

Deepgram is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.

The Deepgram Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models.

Operating Principles:

Deepgram is a research company that productizes as fast as possible. Four Operating Principles shape how we think, decide, and work together:

1. Compress before you communicate. You think it through first, then lead with the bottom line.

2. Resist entropy. You question what already exists before adding more process or tools.

3. Solve the category. You fix recurring problems at the source, not one case at a time.

4. Work at the frontier. You go after problems no one has solved yet.

We look for people who already work this way, and these principles shape how we grow and evaluate our team.

We build AI that millions of people talk to, and we run the company the same way. AI is how we centralize work here, and is core to how we operate.

That means more than using the tools. We expect you to:

• Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster.

• Own the output. AI can speed up the work. It doesn’t make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you.

• Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet.

• Keep pace. Models and tools change every month, and so will your work. You adopt what’s new quickly and drop what no longer earns its place.

We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment.

The opportunity

We are seeking an engineer to join our Applied AI team dedicated to solving the toughest problems in voice AI for enterprise customers.

We work directly with some of the largest companies deploying voice AI - from leading conversational AI and customer experience platforms to restaurant enterprises and technology partners - taking on the problems that don't have off-the-shelf answers. Much of the work is zero to one.

Current focus areas include:

• Ultra-robust ASR in noisy outdoor environments

• Self-service fine-tuning and model adaptation for large enterprise customers

• High-accuracy, domain-aware voice agents, including drive-thru and phone ordering

• Robust scoring and evaluation of agents

• Real-time analytics and operational intelligence

• Deeper, more reliable on-site hardware integration

We partner with several leading enterprises today and continue to expand these partnerships.

What you'll do

• Design, develop, and maintain scalable, high-performance backend systems for our platform

• Improve speech-to-text and text-to-speech quality across an array of environments

• Expand robust scoring and evaluation of our agents

• Fine-tune and adapt models for customer-specific vocabulary and domains

• Optimize the AI pipeline to improve performance in challenging audio environments, robustly handle ambiguous customer requests, and rapidly scale to new domains

• Run experiments to validate the product impact of new functionality

• Collaborate closely with the team to ensure seamless integration between our ML models, backend and client devices

It's important to us that you have

• 4+ years of hands-on experience in developing, implementing, and maintaining cloud infrastructure in production

• Hands-on applied AI/ML experience that goes beyond using LLMs — training, fine-tuning or adapting models yourself

• Experience evaluating AI systems, such as building evaluation or scoring for models or agents

• Excellent problem-solving skills and the ability to adapt to a rapidly evolving startup environment

• Strong communication skills and the ability to work collaboratively with a team

• Willingness to problem solve across our entire product

• Ability to proactively recognize problems and identify solutions to those problems

• Results oriented and care about the impact of your work

It would be great if you had

• Experience working with audio

• Experience with speech-to-text or text-to-speech models

• Applied research experience, or a background as a research engineer

• Experience taking a system from zero to one

• Experience building and maintaining a system that integrates with many 3rd party APIs, particularly Point of Sale systems

• Experience with Python, Kotlin, or TypeScript

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field

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