Materials science company scaling manufacturing excellence and AI-enabled products globally
Syensqo is a 13,000-person materials and specialty chemicals manufacturer headquartered in Brussels, now in an active hiring cycle focused on engineering, manufacturing operations, and security roles across 20+ countries. The tech stack reveals a dual-track operation: legacy industrial control systems (DCS, Allen-Bradley, Instron, AutoCAD) supporting on-site production, paired with cloud infrastructure (Azure, GCP, AWS) and emerging AI adoption (RAG, MLOps, AIOps). Active projects center on manufacturing reliability, equipment downtime reduction, and AI-enabled product features—while pain points cluster around regulatory compliance (asbestos, privacy), equipment reliability, and infrastructure security, signaling investment in both operational resilience and digital risk management.
Notable leadership hires: Cybersecurity Lead, R&I Director, Team Lead
Syensqo develops high-performance materials, specialty chemicals, and biotechnology solutions for automotive, aerospace, healthcare, and consumer goods. The company operates a distributed manufacturing footprint with production facilities requiring on-site control systems and instrumentation (DCS, process automation), complemented by product development and R&D teams. Founded in 2023 as a public company from a legacy industrial base, the organization is expanding its engineering and manufacturing operations capability while simultaneously building out AI and data functions. Current hiring velocity is accelerating, with the largest demand in engineering, manufacturing, and operations roles.
Syensqo is hiring across 21 countries: Canada, US, UK, Italy, Portugal, France, Belgium, Brazil, China, Spain, Germany, India, Chile, Netherlands, New Zealand, Czechia, Thailand, Poland, South Korea, Morocco, and Peru.
Core stack includes SAP for ERP, DCS and Allen-Bradley for industrial control, Python and Java for development, Docker and Jenkins for CI/CD, Azure/GCP/AWS for cloud, and emerging adoption of RAG and MLOps for AI features.
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