Senior Machine Learning Engineer

What Cognite is: Relentless to achieve 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.   About the Opportunity 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. About the Role 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 The Impact you bring to Cognite Key Responsibilities Take product requirements and independently design, train, test, and deploy ML models (e.g., NLP, Vision-Language Models) for document parsing, layout analysis, and entity matching. Write high-quality, scalable production code (Python). Wrap your ML models into robust RESTful or gRPC APIs and integrate them seamlessly into existing industrial master data systems and workflows. Implement and maintain CI/CD pipelines for your models. Navigate complex deployment environments, manage containerized applications (Docker/Kubernetes), and optimize inference bottlenecks. Work with Principal engineers to translate high-level system architectures into concrete, scalable data pipelines and production-ready microservices. Design automated testing for ML pipelines. Monitor deployed models in production for data drift, latency, and accuracy, proactively implementing retraining strategies. Collaborate effectively with product managers to define ML capabilities. Provide code reviews and informal technical mentorship to engineers. Required Skills and Qualifications Bachelor’s or Master's degree in Computer Science, Data Science, Software Engineering, or a related field. 6–10 years of industry experience in software engineering with a strong focus on machine learning, MLOps, and API development. Strong programming skills in Python with experience using backend web frameworks (e.g., FastAPI, Flask, Django) to serve models. Solid expertise in frameworks like PyTorch, TensorFlow, Hugging Face Transformers, or LangChain. Hands-on experience with containerization (Docker, Kubernetes), Linux environments, CI/CD tools (GitHub Actions, Jenkins), and deploying models on cloud platforms (AWS, Azure, or GCP). Solid understanding of software architecture, data structures, and algorithms to ensure performant code. Hands-on experience with distributed data processing frameworks (e.g., Apache Spark, Ray, Dask) and orchestrating complex data workflows (e.g., Apache Airflow, Dagster, Prefect). What Sets your Role Apart Independently define the technical ML/SWE approach for ambiguous feature requests and deliver to production. Recognize the model is a small part of the solution; expertly manage aspects like API rate limiting, asynchronous processing, and robust data routing around ML components. Possess hands-on experience maintaining production ML models and designing resilient systems against poor data quality.. Preferred Qualificatio