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 Machine Learning Engineer, you will be a key contributor to building the models and pipelines that power our industrial contextualization platform. Working closely with senior and staff-level engineers, you will focus on execution—transforming research prototypes into high-performance, real-time applications and building the infrastructure that supports them.
This is an engineering-first role. We seek hands-on builders who write production-grade code, understand system design, and tackle complex infrastructure, treating ML models as software components in a large industrial architecture—not pure data scientists building isolated models.
How you'll demonstrate
Ownership
You love writing clean, readable code and seeing it work. You are deeply curious about how complex ML systems operate in the real world and are passionate about turning raw data into robust, reliable pipelines.
You don't just write research scripts; you want to build durable software. You know how to take a well-defined technical plan, execute it efficiently, and ask the right questions when you hit roadblocks. You prioritize building things the right way the first time over quick, fragile hacks.
You are eager to absorb new paradigms in MLOps, containerization, and model deployment, applying feedback rapidly to improve your craft.
