AI-powered robotic picking for warehouse automation and order fulfillment
Nomagic builds vision-driven robotic systems for warehouse picking and fulfillment. The stack centers on Python, GCP, and Kubernetes—modern ML-ops infrastructure—while active projects span anomaly detection, robot control systems, and deployment tooling at scale. Hiring is engineering-heavy (12 of 16 core staff roles), tilted toward senior and manager levels, and the pain-point list fixates on one problem: scaling robot deployments from prototypes to hundreds of units per year.
Notable leadership hires: Sales Director
Nomagic develops AI robotic picking solutions for e-commerce, retail, and logistics operators. The product automates piece-picking and order fulfillment using computer vision and machine learning to handle millions of item variations. Beyond the robot itself, Nomagic integrates with warehouse management systems and handles returns processing and inventory management. The company is based in Warsaw with operations and hiring across Poland, the United States, and Germany. Current focus spans robot control architecture, anomaly detection during picking, and the operational jump from small pilot deployments to fleet-scale rollouts.
Python, GCP, BigQuery, Kubernetes, Docker, Terraform, Prometheus, and Grafana. The stack reflects ML-ops maturity: orchestration via Kubernetes/Helm/ArgoCD, infrastructure-as-code with Terraform, monitoring with Prometheus/Grafana.
Robot control systems, anomaly detection in picking operations, robot cell prototyping, warehouse management protocols, ERP system implementation, and deployment tooling to scale robot fleets globally.
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