Applied ML Engineer

deepgram; United States; On-site; USD 140,000 - 180,000; Full time

Job summary

Deepgram is seeking an Applied ML Engineer to own and streamline the research-to-production pipeline for real-time speech models. You will work shoulder-to-shoulder with researchers to move models from research notebooks to production systems, focusing on robust training, evaluation, packaging, and safe deployment.

The role spans our hybrid infrastructure, covering GPU data centers and cloud, and relies on in-house tooling that makes translating ideas into production models fast and reliable.

Responsibilities

• Own and streamline the research-to-production pipeline for models.
• Collaborate with research scientists to take models from checkpoint to production safely.
• Hardening training and evaluation workflows, building packaging and deployment paths for production use.
• Ensure production models run across a hybrid training and inference stack (in-house GPUs, data centers, and cloud).
• Leverage in-house tooling to turn research ideas into production models without rewriting code.

Job description

Company OverviewDeepgram

is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are 'Powered by Deepgram', including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram's voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram.

Company Operating RhythmAt Deepgram, we expect an AI-first mindset- AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.

Deepgram's research team produces some of the fastest and most accurate speech models in the world. The hardest, highest-leverage problem is what comes next: turning a promising research result into a model that ships reliably, serves at scale, and keeps its accuracy and latency promises under real production traffic. That path - from a checkpoint that works in a research notebook to a model running across our fleet - is where this role lives.

As an Applied ML Engineer, you will own and streamline the research-to-production pipeline. You'll work shoulder-to-shoulder with research scientists to take their models the last mile: hardening training and evaluation workflows, building the packaging and deployment paths that get new models into production safely, and closing the loop so the next model is faster and easier to ship than the last. You'll work across our custom infrastructure - a hybrid training and inference stack spanning our own GPU data centers and the cloud - and the in-house tooling that lets a research idea become a production model without a rewrite.This is a builder role at the intersection of ML and systems engineering