Foundational Model Research & Engineering Internship: Building Next-Generation Industrial AI

About Cognite

Embark on a transformative journey with Cognite, a global SaaS forerunner in leveraging AI and data to unravel complex business challenges through our cutting-edge offerings including Cognite Atlas AI, an industrial agent workbench, and the Cognite Data Fusion (CDF) platform. We were awarded the 2022 Technology Innovation Leader for Global Digital Industrial Platforms & Cognite was recognized as 2024 Microsoft Energy and Resources Partner of the Year. In the realm of industrial digital transformation, we stand at the forefront, reshaping the future of Oil & Gas, Chemicals, Pharma and other Manufacturing and Energy sectors. Join us in this venture where AI and data meet ingenuity, and together, we forge the path to a smarter, more connected industrial future.

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Our values

Impact: Cogniters strive to make an impact in all that they do. We are result-oriented, always asking ourselves.

Ownership: Cogniters embrace a culture of ownership. We go beyond our comfort zones to contribute to the greater good, fostering inclusivity and sharing responsibilities for challenges and success.

Relentless: Cogniters are relentless in their pursuit of innovation. We are determined and deliverable (never ruthless or reckless), facing challenges head-on and viewing setbacks as opportunities for growth.

The Cognite Atlas AI research team is engaged in world leading work on foundational transformer models for industrial use cases, pushing the boundaries of applied artificial intelligence. While traditional Large Language Models (LLMs) excel at general tasks, they fall short on some critical industrial problems. This is because their training data, sourced primarily from the public internet, lacks the specialized, proprietary knowledge required to understand complex industrial processes, equipment, and data. Cognite has access to this type of data that almost no other product company in the world does.

Our mission is to bridge this gap by developing and scaling the next generation of AI systems trained on domain-specific industrial data. This initiative involves fundamental research into model architecture and pioneering ML engineering to build models that can reason over and understand complex industrial data.

This internship project offers a unique opportunity for exceptional students passionate about Deep Learning and ML Engineering to contribute directly to the core of our AI strategy. The intern will be immersed in the end-to-end lifecycle of foundational model development, from experimenting with novel architectures to building the high-performance infrastructure required to train them. This role is for those who want to build the future of industrial AI from the ground up.

This internship will span 6-8 weeks, commencing in the first week of July. Interns will work collaboratively in pairs of two, fostering a dynamic and supportive learning environment. For students with exceptional outcomes during the internship, continued collaboration is likely to be offered.

Project Scope & Activities

• Foundational Model Research & Development:
• Implementing and experimenting with novel transformer architectures, attention mechanisms, and optimization strategies for industrial data.
• Contributing to the pre-training and validation of large-scale models on massive, proprietary industrial datasets.
• Conducting rigorous analysis and ablation studies to understand model behavior and drive architectural improvements.

• ML Engineering & Scaling:
• Designing and building robust, scalable data pipelines for large-scale model training.
• Developing and maintaining the infrastructure for distributed training across cutting-edge cloud and on-premise GPU clusters.
• Creating sophisticated tools and frameworks for model evaluation, performance profiling, and experiment tracking to accelerate the research and development cycle.

Expected Outcomes

• Contributed directly to the development of a state-of-the-art foundational model tailored for industrial applications.
• Gained significant hands-on experience in large-scale, distributed model training and high-performance computing.
• Designed and implemented critical components of an MLOps pipeline for foundational model research.
• Acquired a deep, practical understanding of the challenges and solutions in training and scaling massive AI models.
• Authored clear technical documentation on model experiments, architectural designs, and engineering systems.

Required Skills & Qualifications

• Machine Learning / AI: Deep theoretical and practical understanding of deep learning, particularly transformer architectures.
• Python: Advanced proficiency in Python and deep experience with at least one major ML framework (e.g., PyTorch, TensorFlow, JAX).
• Software Engineering: Strong fundamentals in data structures, algorithms, and software design principles.
• Problem-Solving: Excellent analytical and problem-solving skills, with a passion for tackling complex, open-ended challenges.
• Collaboration: Ability to work effectively in a team, communicate technical concepts clearly, and adapt to a fast-paced research environment.

Bonus Skills (Nice to Have):

• Experience with cloud platforms (GCP, AWS, Azure) and distributed computing.
• Familiarity with containerization and orchestration technologies (Docker, Kubernetes).
• Experience with high-performance computing (HPC) environments and GPU optimization.
• Contributions to open-source ML or systems software projects.

A snapshot of our many perks and benefits as a Cogniter

• Join an organization of 70 different nationalities 🌐 with Diversity, Equality and Inclusion (DEI) in focus 🤝
• A highly modern and fun working environment with sublime culture across the organization, follow us on Instagram @cognitedata 📷 to know more
• Flat structure with direct access to decision-makers, with minimal amount of bureaucracy
• Opportunity to work with and learn from some of the best people on some of the most ambitious projects found anywhere, across industries
• Join our HUB 🗣️ to be part of the conversation directly with Cogniters and our partners.

Why choose Cognite? 🏆 🚀

Join us in making a real and lasting impact in one of the most exciting and fastest-growing new software companies in the world. We have repeatedly demonstrated that digital transformation, when anchored on strong DataOps, drives business value and sustainability for clients and allows front-line workers, as well as domain experts, to make better decisions every single day. We were recognized as one of CNBC's top global enterprise technology startups powering digital transformation! And just recently, Frost & Sullivan named Cognite a Technology Innovation Leader! 🥇 Most recently Cognite Data Fusion® Achieved Industry First DNV Compliance for Digital Twins 🥇

Apply today!

If you're excited about the opportunity to work at Cognite and make a difference in the tech industry, we encourage you to apply today! We welcome candidates of all backgrounds and identities to join our team. Please do not hesitate to contact our Talent Acquisition team with any questions -

We encourage you to follow us on Cognite LinkedIn; we post all our openings there.

Equal Opportunity

Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment; everyone we hire will receive the same level of consideration for training, compensation, and promotion.

We ask for gender as part of our application because we want to ensure equal assessment in the recruitment process. Your answer will help us reach this commitment! However, the question about gender is optional and your choice not to answer will not affect the assessment of your application in any way.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.