Deepgram

Applied ML Engineer

Remote Engineering role with clear candidate location fit.

PostedJul 9, 2026

Eligible countries1 accepted country

Seniority signalOpen level

Work settingRemote

Accepted candidate locations

USA

Role overview

Readable role content extracted into sections for faster review.

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.

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.

Strong software engineering fundamentals, with proficiency in Python and experience writing production-quality, well-tested ML code.
Hands-on experience taking ML models from research or prototype stage into production at scale — not just training models, but shipping and operating them.
A working understanding of the modern deep learning stack (e.g., PyTorch) and the realities of training, evaluating, and serving large models.
Experience building ML pipelines and tooling — training orchestration, evaluation harnesses, model packaging, deployment, or CI/CD for models.
Familiarity with serving and inference optimization — latency, throughput, batching, and resource efficiency for production model workloads.
Comfort operating across distributed systems and GPU compute, whether in the cloud, on bare metal, or both.
A collaborative, builder mindset — you can partner with researchers, scope an ambiguous problem, and drive it to a measurable result.

Experience with the research-to-production handoff specifically — building the systems and conventions that let research and engineering iterate together quickly.
Background in speech, audio, or other real-time/streaming ML domains.
Experience designing automated model evaluation and release-gating systems, including regression detection across model versions.
Familiarity with hybrid infrastructure spanning on-premise GPU clusters and cloud, and with workload orchestration across them.
Experience with inference optimization techniques (quantization, distillation, compilation, or runtime tuning) for production serving.
A track record of building internal platforms or developer-facing tooling that measurably improved how a team ships models.

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