Cognite operates at the forefront of industrial digitalization, building AI, and data solutions that solve the world's hardest, highest-impact problems. With unmatched industrial heritage and a comprehensive suite of AI capabilities, including low-code AI agents, Cognite accelerates the digital transformation to drive operational improvements.
We thrive in challenges. We challenge assumptions. We execute with speed and ownership. If you view obstacles as signals to step forward - not backwards - you'll feel right at home here.
Our Moonshot is bold: Unlock $100B in customer value by 2035, and redefine how global industry works. Join us in this venture where AI and data meet ingenuity, and together, we will forge the path to a smarter, more connected industrial future.
We are building the next generation of contextual AI for Industrial Operations. Our team focuses on transforming unstructured, complex industrial data—ranging from technical manuals to complex piping and instrumentation diagrams (P&IDs)—into structured, actionable intelligence. We leverage state-of-the-art Deep Learning, Generative AI, and Computer Vision to drive efficiency, safety, and operational excellence.
As a Senior Machine Learning Engineer, you will be the engine of our contextualization initiatives, taking independent ownership of complex ML features from conception to production. You will bridge the gap between data science and software engineering, building the models and the surrounding infrastructure that parse complex industrial documents, extract multimodal entities, and interpret intricate engineering diagrams.
To be clear: this is an engineering-first role. We are not just looking for researchers to build isolated models; we need builders who write production-grade code, build robust APIs, and solve complex infrastructure problems. You will treat ML models as software components integrated into a highly scalable archite
How you'll demonstrate
Ownership
- System-minded, focused on maintainability, rigorous testing, and automated pipelines for reliable ML production.
- Thrives in ambiguity, independently defining the technical path, selecting tools judiciously, and driving solutions to completion.
- Elevates team code quality through constructive reviews and informal mentorship, bridging the gap between research and production
