
Building the future of energy markets Vortexa tracks more than $3 trillion of waterborne energy trades per year in real-time, providing energy and shipping companies with the most complete picture…

Building the future of energy markets Vortexa tracks more than $3 trillion of waterborne energy trades per year in real-time, providing energy and shipping companies with the most complete picture…
Product: Real-time energy and freight analytics platform for crude, refined products, LPG and LNG
Tech: Vessel-tracking, market data and machine learning models
Founding year: 2016
Founders: Fabio Kuhn and Etienne Amic
Recent financing: Series C $34M (Jan 2024) and debt financing Oct 2024
Energy market intelligence and freight analytics for trading and chartering decisions.
2016
Software Development
$34M
Debt financing reported in Oct 2024
“Backers include Morgan Stanley Expansion Capital, Notion Capital, monashees and others”
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Processing thousands of energy data points per second from diverse operational sources, handling massive volumes of energy data while running sophisticated classification and anomaly detection models in real-time, maintaining comprehensive data lineage, and delivering insights through high-performance platforms used by energy operators globally requires exceptional engineering and scientific expertise. This processing demands models that can withstand the scrutiny of energy analysts and traders, operations teams, and regulatory bodies, with the performance, stability, and reliability that critical energy systems require.
The Data Platform Team is responsible for all machine learning operations across our energy data ecosystem. We work with everything from raw sensor data from millions of energy assets to complex operational datasets, generating high-value predictions such as equipment failure detection, energy demand forecasting, operational anomaly identification, predictive maintenance scheduling, and system optimization recommendations.
The team has built a comprehensive suite of statistical and machine learning models that enable us to provide the most accurate and actionable insights into energy operations. We take pride in applying cutting-edge research to real-world energy challenges in a robust, scalable, and maintainable way. The quality of our models is continuously validated by experienced in-house energy analysts and traders and domain experts to ensure reliability of our predictions.
You'll be instrumental in designing and building ML infrastructure and applications to propel the design, deployment, and monitoring of existing and new ML pipelines and models. Working with software engineers, data scientists, and energy analysts and traders, you'll help bridge the gap between research experiments and production energy systems by ensuring 100% uptime and bulletproof fault-tolerance of every component of our ML platform.
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