Machine Learning Engineer

RealAdvisor is one of the leading digital platforms in the European real estate ecosystem. Founded in Switzerland, we now support 5,000+ real estate agencies and attract 10+ million users per year across Europe. We are scaling fast across multiple markets with a clear mission: help real estate professionals grow through data, transparency and smart technology. At RealAdvisor, we value ownership, autonomy and impact. Role Overview We are looking for a Machine Learning Engineer to take end-to-end ownership of our geospatial data products. This is not a model-tuning role. You will design, build, and operate the full data lifecycle behind our real-estate insights — from orchestrating complex pipelines on Kubernetes to delivering high-performance geospatial visualizations with Mapbox. This is a builder role. You should be as comfortable debugging an Airflow DAG as you are computing spatial statistics or optimizing analytical queries. You’ll have full autonomy over a modern, high-performance stack: Orchestration & Operations: Airflow, Kubernetes, Docker Data Storage: Postgres (PostGIS) for spatial workloads; ClickHouse for large-scale analytics Programming: Production-grade Python Requirements Key Responsibilities End-to-End Pipelines: Design and deploy automated pipelines (ETL) using Airflow to ingest diverse datasets (Real Estate transactions, listings, geo-data). Geospatial Analytics: Compute advanced spatial metrics (e.g., pricing honeycombs, average prices per neighborhood) using PostGIS and Python. Infrastructure Management: Maintain and optimize the Kubernetes environment where your pipelines and models run. Visualization Engineering: Transform your analytical outputs into interactive map layers (Vector Tiles) that integrate seamlessly with Mapbox on our frontend. What We’re Looking For Truly Full-Stack (Data): You don’t believe data scientists should stop at notebooks. You own infrastructure, deployment, and reliability because that’s how real value is delivered. Strong Geospatial Intuition: You understand projections, polygons, spatial joins, and how to visualize density and patterns effectively. Engineering Mindset: You write clean, modular, maintainable Python code designed for production — not one-off scripts. Experience: 3+ years in a Data Science or Data Engineering role, with hands-on ownership of production systems. Infrastructure Proficiency: Solid experience with Docker and Kubernetes, including debugging and troubleshooting live environments. Database Expertise: Advanced SQL, especially with PostGIS or similar spatial extensions. Experience with ClickHouse is a strong plus. Orchestration Mastery: Deep hands-on experience with Airflow for building and maintaining reliable data pipelines. Fully remote role with flexible working hours. Close collaboration with leadership and go-to-market teams. International, ambitious, and collaborative team culture. Benefits Fully remote role with flexible working hours. Close collaboration with leadership and go-to-market teams. International, ambitious, and collaborative team culture. Originally posted on Himalayas