Applied ML Engineer

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.

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.

Nice-to-Haves

• 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.