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Scandit's ShelfView is a machine-learning-powered platform for retail that delivers real-time shelf visibility and operational efficiency. The platform is scaling rapidly, processing billions of product-location updates annually and supporting some of the largest retail intelligent deployments globally.
As a Senior Backend Engineer, you will own complex engineering problems end-to-end on the backend systems powering ShelfView's product recognition and store-monitoring pipeline. Your responsibilities include:
• Design and operate distributed systems that transform store imagery into real-time, actionable alerts
• Evaluate and implement workflow-orchestration infrastructure for ML pipelines (e.g., Temporal)
• Make strategic infrastructure and GPU-capacity tradeoffs for model serving at scale
• Extend and harden the identity and multi-tenancy platform for larger, security-conscious enterprise customers
• Load- and stress-test the platform against the largest deployments, identifying and closing gaps
• Deepen production observability using distributed tracing, metrics, and structured logging
• Design and evolve service APIs as the platform grows
• Mentor other engineers and help set technical direction as the team scales
You will collaborate closely with AI/ML researchers and engineers to productionize innovations, and work across infrastructure, backend, data, and frontend domains.
Tech stack: Python/Django, PostgreSQL, Temporal, PubSub, GCP/AWS (including Vertex AI for model serving), OpenTelemetry, Grafana Tempo/Jaeger, GitLab.
Required: 5+ years shipping backend software in the cloud; hands-on experience with workflow orchestration (Temporal, Celery, Airflow, Dagster, or similar); identity/auth systems (SSO, OAuth/OIDC, RBAC) and multi-tenant architectures; observability tooling for production debugging; Python as your strongest language; databases (relational and document-oriented), service-oriented architectures, cloud data pipelines, Docker, Kubernetes; Infrastructure as Code (Terraform); production systems at scale; automation testing and CI/CD; comfort with AI coding tools; T-shaped experience with depth in at least one area (infrastructure, backend, data, frontend); willingness to work cross-functionally.
