Pulppo is building Shopify for the real estate brokerages in Latin America. We provide them with performance analytics software, automatic online property listings, a valuation tool and a CRM. We will charge 20% of the 166 billions dollars in annual commissions they generate. From our integrated tool we automate non-value added activities, and give visibility and transparency to agents (CRM) and customers (buyer collaboration, seller dashboard). By integrating the entire process, we empower agents to deliver an improved customer journey and more efficient process.
HR full Cycle executive
Pulppo has the great challenge of being a technological partner of a low-tech market!
At Pulppo, culture is key. As the first HR hire, you will be responsible for proactively supporting the founders in growing the strong culture our early team has created. You will have the exciting tasks of:
If you are interested, send your application and let us meet you!
About the person we are looking for. You are (or have):
About the interview
At Pulppo, we are changing the Proptech landscape by improving the way users buy and sell properties. We created the first tech-enabled real estate broker for Latin America.
We were frustrated with the fact that, while buying a property should be a simpIe and joyful process, it’s usually complicated and full of frustrations. So we set off to change it.
By bringing together the best agents in Mexico, and providing them with all the tools they need to be the best at their jobs, we create a unique experience for end users where buying or selling a property is a transparent and centralized process.
This journey is fast paced, exciting and not for everyone. Apply if you have what it takes and looking for a challenge.
Our infrastructure is based on a microservice architecture. Each of the microservices use modern web technologies such as typescript, react, next.js and tailwind for the frontend and typescript, express.js and node.js for the backend. Even some of our microservices run in python (mostly for data-driven tasks or ML models). For databases we prefer using mongoDB, but we are open to the idea of using postgresql or other relational databases if they better solve the problem. We are also developing some machine learning and computer vision applications using keras, tensorflow and opencv (even some tensorflow.js for frontend apps). On the infrastructure side of things, all our microservices are dockerized and running on kubernetes (some on lambda functions/serverless).
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