Principal ML 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 developing next-gen contextual AI for Industrial Operations, transforming unstructured industrial data (manuals, P&IDs) into structured, actionable intelligence using Deep Learning, Generative AI, and Computer Vision for efficiency, safety, and operational excellence. The Principal ML Engineer designs, builds, and deploys complex, scalable ML systems, bridging data science and software engineering to transform research prototypes into high-performance, real-time applications. This role is fundamentally focused on engineering. We seek individuals who are builders capable of writing production-grade code, designing distributed systems, and resolving complex infrastructure challenges, rather than solely researchers developing isolated models. The successful candidate will treat machine learning models as integrated components within a highly scalable software architecture designed to withstand the demands of industrial operations. How you’ll demonstrate Ownership You are a seasoned builder who sets the gold standard for engineering excellence on your team. You design fault-tolerant, scalable ML architectures from scratch and actively guide the team in adopting the latest best practices for production-grade AI. You balance technical perfection with business realities. You anticipate macro-level system bottlenecks, make tough architectural trade-offs, and seamlessly translate overarching business goals into concrete, executable technical strategies. You are a trusted advisor. You take ownership of large, complex projects from conception through deployment, actively mentor mid-level engineers, and define the technical standards that keep the team moving fast and safely. The Impact you bring to Cognite Key Responsibilities Study, transform, and deploy data science prototypes into production environments, ensuring they are scalable, efficient, and maintainable. Architect and write high-quality, scalable, and testable production code (Python, Scala, C++, or Java). Build the robust APIs, distributed big data pipelines, and orchestration layers required to integrate AI into existing industrial master data systems. Navigate complex deployment environments. You will optimize inference bottlenecks, manage containerized deployments, and ensure our systems can handle massive volumes of unstructured industrial data reliably. Research and implement advanced ML algorithms (e.g., in NLP, Computer Vision) and extend existing ML libraries and frameworks. Run tests, perform statistical analysis, and monitor deployed models for performance, drift, and accuracy, implementing retraining strategies as needed. Work closely with data scientists, software engineers, and product managers to align ML solutions with business goals. Provide technical mentorship to junior team members, guide code reviews, and help define technical standards for the team.  Required Skills and Qualifications Bachelor’s or Master's degree in Computer Science, Data Science, or a related field (PhD is often preferred for higher-level roles). 10+ years of industry experience in machine learning and software development. Strong programming skills in Python, C++, or Java. Expertise in frameworks like PyTorch, TensorFlow, Keras, or Scikit-learn. Familiarity with MLOps best practices and cloud platforms such as AWS, Azure, or GCP. Solid understanding of data structures, algorithms, and software architecture. Preferred Qualifications  Deep expertise designing complex AI systems from the ground up, including fine-tuned multimodal models for dense diagrams (P&IDs, CAD), Agentic AI for multi-step reasoning, and advanced Graph RAG/Knowledge Graphs. Experience architecting modern lakehouses (e.g., Delta Lake, Apache Iceberg) to process and manage massive, complex datasets specific to manufacturing, supply chain, or OT (Operational Technology) environments. Proven ability to optimize large language models for maximum throughput and low latency, with experience deploying AI into highly secure, on-premises, or edge environments. What Sets Senior Roles Apart The abi