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.
About the RoleAs 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 OwnershipYou 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.
- Build, test, and maintain robust data pipelines for large-scale industrial datasets.
- Assist in deploying data science prototypes to production.
- Write high-quality, testable Python code, primarily creating RESTful/gRPC APIs for ML capabilities.
- Containerize applications (Docker) and navigate Linux to handle massive volumes of unstructured industrial data.
- Run experiments, fine-tune existing foundational models (NLP, Vision-Language), and evaluate ML libraries to enhance document parsing and entity matching.
- Write rigorous unit/integration tests and monitor deployed models for drift and performance.
- Collaborate closely with senior staff (engineers, data scientists, product managers) to deliver features on the technical roadmap.
Required Skills and Qualifications
- Bachelor’s or Master's degree in Computer Science, Data Science, Software Engineering, or a related field.
- 3–6 years of industry experience in software engineering with a strong focus on machine learning, or vice versa.
- Strong programming skills in Python, with a proven ability to write clean, modular, and testable code.
- Solid hands-on experience with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain.
- Experience working with large datasets, writing complex SQL queries, and hands-on familiarity with data processing libraries or frameworks (e.g., PySpark, Pandas at scale, or Dask)
- Familiarity with MLOps basics, containerization (Docker), Linux command-line tools, and fundamental cloud services (AWS, Azure, or GCP).
- Solid understanding of core computer science fundamentals, data structures, and algorithms.
- The ability to take a well-defined technical problem or architecture plan from a principal engineer and write highly efficient, bug-free code to solve it.
- A rapid trajectory in absorbing new paradigms (like vector databases or agentic workflows) and efficiently applying them to massive volumes of real-world industrial data without crashing the system.
- A commitment to treating ML code like production software, never skipping the tests or the documentation.
- Academic or early industry experience with Vision-Language Models (VLMs) or Computer Vision (e.g., document layout analysis).
- Hands-on familiarity with basic RAG pipelines and Vector Databases (e.g., Pinecone, Milvus, Qdrant). Exposure to agentic frameworks (e.g., LangGraph).
- Exposure to modern columnar data (e.g., Parquet) or data lakes (e.g., Delta Lake, Apache Iceberg). Understand efficient querying/chunking of large unstructured data for ML.
- Experience with highly unstructured data, including text and JSON extraction from messy PDFs using OCR. OT dataset exposure is a plus.
- Experience serving models via backend web frameworks (FastAPI/Flask). Comfortable troubleshooting Linux permissions, fonts, and dependencies.
- Impact 2025
- Cognite's Industrial AI: Moonshot
- We’re globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.
Skills Required
- Bachelor's or Master's degree in Computer Science, Data Science, Software Engineering, or a related field
- 3-6 years of industry experience in software engineering with a strong focus on machine learning, or vice versa
- Strong Python programming skills and ability to write clean, modular, testable code
- Hands-on experience with PyTorch, TensorFlow, Hugging Face, or LangChain
- Experience working with large datasets and writing complex SQL queries
- Hands-on familiarity with data processing libraries or frameworks such as PySpark, Pandas at scale, or Dask
- Familiarity with MLOps, Docker, Linux command-line tools, and fundamental cloud services such as AWS, Azure, or GCP
- Understanding of computer science fundamentals, data structures, and algorithms
- Experience with Vision-Language Models or Computer Vision, such as document layout analysis
- Familiarity with basic RAG pipelines and vector databases such as Pinecone, Milvus, or Qdrant
- Exposure to agentic frameworks such as LangGraph
- Exposure to Parquet, Delta Lake, or Apache Iceberg
- Experience with unstructured data, text and JSON extraction from PDFs using OCR
- Exposure to OT datasets
- Experience serving models with FastAPI or Flask
- Comfort troubleshooting Linux permissions, fonts, and dependencies
Cognite Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cognite and has not been reviewed or approved by Cognite.
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Affordable Benefits — Healthcare premiums for employees and dependents are often fully covered, reducing out‑of‑pocket costs. Feedback suggests this makes the total rewards feel competitive in key markets.
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Parental & Family Support — Paid parental leave for primary and secondary caregivers is described as generous. This signals a family‑friendly approach that many consider a standout perk.
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Leave & Time Off Breadth — Unlimited PTO with a company‑wide year‑end shutdown offers ample time away from work. Flexible time‑off options are consistently highlighted across U.S. roles.
Cognite Insights
What We Do
Cognite is an AI company that delivers industrial software to improve the production efficiency of Energy, Process Manufacturing, and other industrial companies. We deliver an Industrial DataOps platform that liberates siloed data and empowers our customers to solve some of their most complex business problems with AI-powered solutions. The typical solutions we enable drive innovative new ways to approach Data Exploration, Digital Operator Rounds, Production Optimization, Turnaround Planning, and Root Cause Analysis. We do this by automating and scaling industrial data contextualization of various sources (such as time series, engineering diagrams, equipment logs, maintenance records, 3D facility models, images, large point clouds, and more). We use AI and other tools to find and map the meaningful relationships between the data across these various sources. In addition, we provide intuitive tools that enable efficient use of analytics and automated workflows, as well as prebuilt AI capabilities and a low-code industrial agent builder, Cognite Atlas AI, that enables AI to carry out more complex operations with greater accuracy.
Why Work With Us
Employees at Cognite are pushing the envelope with the latest cloud technology, scaling industrial applications across hundreds of assets, revolutionizing industrial data models, and working with robotics. Cogniters are fast, creative, and resilient. We keep the energy high and fun, learning from our mistakes and celebrating our victories together.
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