As a Senior Machine Learning Engineer on the Autonomous Defense team, you'll be the person who gets our defensive models into production and keeps them there. Our AI researchers define how NodeZero's agents should reason. You build the pipelines, serve infrastructure, and release machinery that turns that research into something running against thousands of customer tenants every day.
This is the seam where most AI products fail. A model that performs well in a notebook is not a capability. It becomes one when there is a reproducible training pipeline, an evaluation suite that runs in CI, a versioned release path with canaries and rollback, monitoring that catches regressions before customers do, and a cost and latency profile the business can afford. That is your work.
You'll work directly with our AI researchers and closely with backend and infrastructure engineers. You are not being hired to do research, and you are not being hired to do generic platform work. You own the path from model to production.
Build and own the training and post-training pipelines — data preparation, fine-tuning and preference optimization runs, experiment tracking, artifact management, and reproducibility.
Build the inference and serving layer: model gateway with provider routing and fallback, regional pinning for data residency, batching, and caching.
Own the release path for model-layer artifacts: version prompts, model selections, and tool definitions as deployable config; run shadow and canary deployments by tenant; make rollback fast and boring.
Build monitoring for the model layer — behavioral drift, regression detection, output quality signals, latency, and per-tenant token and cost accounting with budget enforcement.
Build the data and context pipelines that feed inference, including retrieval and embedding infrastructure over attack path, configuration, and remediation data, with tenant isolation enforced end to end.
Optimize cost and latency across the inference path, and make the tradeoffs visible so product decisions are made with real numbers.
Develop core product features in ETL and GraphQL where model outputs, run history, and evaluation results need to reach the product and internal tooling.
Partner with researchers to move prototypes into production, and feed production constraints and failure data back into research direction.
