
Ganiga Innovation, led by founder and CEO Nicolas Zeoli, stands out as a leader in technological innovation for sustainable urban waste management. Through the implementation of cutting-edge solutions based on artificial intelligence, we address the critical challenges associated with waste treatment, delivering tangible results. What We Do: Smart Waste Sorting: Leveraging machine learning algorithms, Ganiga simplifies waste sorting by efficiently recognizing and separating materials. Intelligent Bins: Our sensor-equipped bins allow for dynamic, real-time waste management, optimizing efficiency in the disposal process. Reduced Environmental Impact: Through responsible waste disposal practices, Ganiga actively contributes to reducing environmental impact, promoting sustainable waste management. Statistics and Data: Operational Cost Reduction: Our technology optimizes operational processes, lowering management costs for businesses and involved institutions. Increased Recycling Rates: Ganiga's solutions incentivize a significant rise in recycling rates, contributing to more efficient resource management. Successful Collaborations: Key partnerships with entities such as Enel, Unicoop.fi, GeoFor, and academic institutions like the University of Pisa underscore the recognition of our effectiveness. Ganiga Innovation positions itself as a catalyst for cutting-edge waste management, combining technology, sustainability, and concrete results.

Ganiga Innovation, led by founder and CEO Nicolas Zeoli, stands out as a leader in technological innovation for sustainable urban waste management. Through the implementation of cutting-edge solutions based on artificial intelligence, we address the critical challenges associated with waste treatment, delivering tangible results. What We Do: Smart Waste Sorting: Leveraging machine learning algorithms, Ganiga simplifies waste sorting by efficiently recognizing and separating materials. Intelligent Bins: Our sensor-equipped bins allow for dynamic, real-time waste management, optimizing efficiency in the disposal process. Reduced Environmental Impact: Through responsible waste disposal practices, Ganiga actively contributes to reducing environmental impact, promoting sustainable waste management. Statistics and Data: Operational Cost Reduction: Our technology optimizes operational processes, lowering management costs for businesses and involved institutions. Increased Recycling Rates: Ganiga's solutions incentivize a significant rise in recycling rates, contributing to more efficient resource management. Successful Collaborations: Key partnerships with entities such as Enel, Unicoop.fi, GeoFor, and academic institutions like the University of Pisa underscore the recognition of our effectiveness. Ganiga Innovation positions itself as a catalyst for cutting-edge waste management, combining technology, sustainability, and concrete results.
What they do: AI- and robotics-based smart waste-management hardware + SaaS (product family: Hoooly)
Headquarters: Bientina (Pisa), Italy
Founder / CEO: Nicolas Lorenzo Zeoli
Employees (reported): 17
Notable partners / clients: Google, Autogrill, airports (e.g., Bologna, Venice), Unicoop, University of Pisa
| Company |
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Urban and commercial waste management, recycling optimization
Software Development
Recorded on company profiles as the most recent funding event
“Investors listed on public profiles include Google, Surge, NextSTEP and individual investor David Bonsignori”
☁️ Cloud & ML Engineer (GCP)
(hands-on, no fuffa)
GANIGA è una startup deep-tech che costruisce sistemi AI per trasformare oggetti, immagini e flussi fisici in dati intelligenti .
Lavoriamo su computer vision, ML, edge + cloud e piattaforme dati scalabili.
Google Cloud Platform è il nostro core.
Cerchiamo una persona tecnica che metta le mani sull’infrastruttura e ci aiuti a far scalare modelli e dati dal mondo reale alla produzione.
Non cerchiamo titoli. Cerchiamo .
Cosa farai
Quello che costruisci va online davvero .
Stack (vero)
GCP · GKE · Cloud Run · Pub/Sub · GCS · BigQuery
Docker · Kubernetes · Terraform · Python
ML workloads · GPU · Edge ↔ Cloud
Chi cerchiamo
Mid-Senior o Senior: contano le mani, non gli anni.
Come lavoriamo
RAL: 60k–80k , in base a quello che sai fare davvero.