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. You won’t just hand models off; you’ll own the mechanism that makes shipping models repeatable, measurable, and fast. It’s a great fit whether you’re a hands-on senior engineer who wants to go deep on the productionization problem, or a staff-level technical leader who wants to define how Deepgram builds and delivers models from research to scale. We’ll set the level to your experience.

What You’ll Do

• Own the research-to-production pipeline: take research checkpoints and turn them into production models, defining the repeatable path from a working result to a deployed, monitored, scaled service.
• Partner directly with research scientists to productionize new models — translating experimental training and evaluation code into robust, reproducible, well-tested workflows.
• Build and extend the tooling and abstractions that let researchers and engineers move models through training, evaluation, packaging, and deployment with minimal friction and maximal reproducibility.
• Design and own model release gates — automated evaluation, regression detection, and quality/latency/throughput checks that decide whether a model is ready to ship.
• Optimize models and serving for production: efficient inference, batching, memory and latency tuning, and the profiling work that turns a research model into something that performs economically at scale.
• Strengthen the build and delivery layer for models on our custom infrastructure, spanning our GPU compute and cloud environments, so that shipping a model is fast, safe, and observable.
• Establish benchmarking and validation that runs consistently from model development all the way through production, so performance and quality regressions are caught early.
• Build the feedback loop: instrument production model behavior, surface what’s working and what isn’t, and feed it back to research to accelerate the next iteration.

You’ll Love This Role If You

• Believe the last mile from research to production is the most important — and most underrated — problem in applied ML, and you want to own it.
• Get satisfaction from turning a fragile, brilliant research prototype into something reliable that serves real traffic.
• Like working at the seam between research and engineering, fluent enough in ML to partner with scientists and rigorous enough in systems to ship at scale.
• Treat infrastructure and tooling as a product — you want researchers to move faster because of what you built.
• Care about reproducibility, evaluation rigor, and measurable quality, not just getting a model out the door.
• Want to ship, not just publish — you measure impact by what’s running in production.

• Are you interested in this position?

   

  Apply by clicking on the “Apply Now” button below!

   

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