Senior Machine Learning Engineer (Defensive Agent)

Join NodeZero as a Senior Machine Learning Engineer on the Defensive Agent team. You will be responsible for taking our defensive models from research to production, building and owning the training and post-training pipelines, inference and serving layer, release path for model-layer artifacts, and monitoring for the model layer. You will work closely with AI researchers and backend/infrastructure engineers, and have a direct impact on the success of our AI products. Enjoy a flexible work environment, growth opportunities, and an inclusive and diverse team culture.

• Construire et posséder les pipelines d'entraînement et de post-entraînement, y compris la préparation des données, les ajustements fins et l'optimisation des préférences.
• Développer l'infrastructure d'inférence et de service, y compris la passerelle de modèle avec le routage des fournisseurs et le basculement.
• Assurer le chemin de publication pour les artefacts de couche de modèle, y compris les déploiements en ombre et en canari par locataire.

• Solid proficiency in SQL and experience with production data pipelines
• Experience building applications on cloud computing platforms such as AWS, Azure, GCP, using container technologies such as Docker and Kubernetes
• Experience building and operating inference or model-serving infrastructure in production, including latency and cost optimization
• Demonstrated experience taking ML or LLM-backed systems from prototype to production and operating them
• 5+ yrs professional software engineering experience, with strong production Python
• Bachelor's Degree in Computer Science, Computer Engineering or related field, or equivalent practical experience
• Hands-on experience with ML pipelines and tooling: training or fine-tuning workflows, experiment tracking, artifact and model registries, and reproducible data preparation
• Experience operationalizing LLM or agentic systems specifically
• Hands-on post-training experience: supervised fine-tuning, distillation, preference optimization, or RL, including the infrastructure around the runs
• Experience with model gateways or multi-provider routing, and with self-hosted or customer-hosted inference (vLLM, TGI, Bedrock, or similar)
• Experience with GPU infrastructure, quantization, or inference optimization
• Experience with database architectures including relational (PostgreSQL) and graph (Neo4j), and with GraphQL backends
• Experience with observability tooling (Datadog, Prometheus, Grafana) and distributed tracing, including tracing across model calls
• Experience shipping ML into regulated, air-gapped, or customer-controlled environments, or under compliance regimes like FedRAMP