Fullstack Software Engineer - Core

Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require. Sitting above data platforms, cloud infrastructure, and AI services, Dataiku connects the full enterprise AI stack — empowering organizations to run AI across multi-vendor environments with centralized governance. The world’s leading companies rely on Dataiku to operationalize AI and run it as a true business performance engine delivering measurable value. For more, visit the Dataiku blog , LinkedIn , X , and YouTube . Why Engineering at Dataiku? Dataiku’s on-premise, cloud, or SaaS-deployed platform connects many data science technologies, and our technology stack reflects our commitment to quality and innovation. We integrate the best of data and AI tech, selecting tools that truly enhance our product. From the latest LLMs to our dedication to open source communities, you'll work with a dynamic range of technologies and contribute to the collective knowledge of global tech innovators. You can find out even more about working in Engineering at Dataiku by taking a look here . Here are some useful links so you preview what we do at Dataiku: Dataiku's Key Capabilities ; Dataiku's Github , you can also take a look at the Gallery , a public instance showcasing some example projects (note editing is very limited and will be regularly reset). Our product is called Dataiku DSS which stands for Dataiku Data Science Studio. If you’d like to know more about it, you can watch the demo here or try the free version here . How you’ll make an impact This position is either onsite/hybrid from our Amsterdam office or full remote from any part of Netherlands. As a Fullstack Engineer, you’ll contribute to building Dataiku DSS core features by joining one of the following themes: Data Preparation : Develop tools for data integration, transformation, and cataloging, as well as Jupyter notebooks, SQL workbenches, and APIs. Add new capabilities for data integration, transformation, and visualization using LLMs Optimize workflows for large-scale datasets and improve database support Strengthen developer tools like Jupyter notebooks and SQL workbenches Tech stack: Java, Python, AngularJS, Angular AI Machine Learning : Collaborate on next-gen AI features, from statistics and time series forecasting to LLM inference. Build next-generation features like cross-provider LLM APIs or integration with the latest Machine Learning models Collaborate with research teams on innovative machine learning experiments Tech stack: Python, scikit-learn, TensorFlow, PyTorch, Java, Angular, AngularJS Data Consumption: build the experience that makes data accessible and actionable for everyone. Enhance our collaborative Workspaces to organize and explore charts, dashboards, datasets and more Create innovative interfaces that answer business questions and surface relevant insights using LLMs Tech stack: Angular, AngularJS, Java, Python Data Visualization : elevate our data visualization capabilities with high-performance charting tools and dashboards. Develop new chart types and optimize visualization performance Enhance dash boarding capabilities to deliver fast and flexible experiences Tech stack: React, AngularJS, Angular, D3.js, ECharts, Node.js, Java, Spring MLOps : Develop back-end capabilities for automating and monitoring ML model lifecycles while supporting cross-functional collaboration. Build tools to automate retraining, monitoring, and deployment of ML models Enhance collaboration features for ML stakeholders Tech stack: Python, Java, Angular, Angular.JS, Kubernetes, Docker Platform : Focus on scaling and securing the platform while improving cloud integrations and supporting a wide array of data sources and engines. Optimize process ... (truncated, view full listing at source)