AI-powered procurement and order platform for collision parts supply chains
Partly builds a foundational AI model for automotive collision parts procurement and order management. The engineering stack spans Python, PyTorch, and scikit-learn for model work, paired with Rust, Kubernetes, and PostgreSQL for infrastructure—a mix suggesting both ML-heavy and production-grade systems thinking. Hiring velocity is accelerating (30 roles posted in 30 days) across engineering, product, and sales, but internal pain points center on scaling recruiting workflows and maintaining hiring quality during rapid growth, indicating a team outpacing its hiring infrastructure.
Notable leadership hires: Head of Legal
Partly connects automotive collision parts suppliers, repairers, and procurement teams through an AI-driven platform spanning sourcing, order management, and supply chain visibility. The company is led by automotive industry veterans and engineers with backgrounds in aerospace, consumer tech, and high-performance computing. Operations span Christchurch (core engineering), the UK (main office), and the US (expansion), with hiring extending to New Zealand, Australia, Philippines, and Indonesia. Active development tracks core SaaS and API infrastructure, repairer onboarding, internal and third-party sales channels, and talent operations—reflecting both product expansion and organizational scaling challenges.
Python, PyTorch, scikit-learn, Pandas, PostgreSQL, Rust, Kubernetes, Terraform, ArgoCD, GCP, and Vanta. The mix indicates ML-first development for the AI model layer and production-grade infrastructure for reliability and scale.
Christchurch, New Zealand (core engineering hub). Main office in the UK, with active expansion into the United States. Hiring also extends to Philippines, Indonesia, and Australia.
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