Applied ML Engineer

Role Description

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.
• 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.

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.

Requirements

• 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.

It Would Be Great if You Had

• 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.