---
title: 'Machine Learning Engineer at Cognite - AI for Industry'
canonical: 'https://feeny.ai/job/machine-learning-engineer-cognite-ai-for-industry-bengaluru-qjxccj0gwgm6'
type: 'job'
last_seen: '2026-10-29'
---

# Machine Learning Engineer at Cognite - AI for Industry

- **Company:** Cognite - AI for Industry
- **Location:** Bengaluru, India
- **Posted:** 2026-10-26
- **Last confirmed live:** 2026-10-29
- **Apply:** https://job-boards.eu.greenhouse.io/cognite/jobs/5016416101

## Job description

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](https://www.cognite.com/en/company/newsroom/cognite-s-industrial-ai-momentum-soars-driving-ecosystem-impact-at-global-conference) 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  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.

The Impact you bring to Cognite

## Key Responsibilities

- 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.

What Sets you Role Apart

- 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.

## Preferred Qualifications / Good to Haves

- 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.

Learn more about us

- [Impact 2025](https://www.cognite.com/en/resources/blog/key-highlights-from-impact-2025)
- [Cognite's Industrial AI: Moonshot](https://www.cognite.com/en/company/newsroom/cognite-s-industrial-ai-momentum-soars-driving-ecosystem-impact-at-global-conference)
- We’re globally recognized domain experts with an international presence that spans Phoenix, Houston, Oslo Tokyo, Bengaluru, and Abu Dhabi.

## 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.

## About Cognite - AI for Industry

## Company Overview
- **One-liner**: Cognite delivers industrial AI software that unifies siloed operational data to help energy, process manufacturing, and other industrial companies improve production efficiency and scale AI solutions.
- **Entity Type**: Private (backed by Aker ASA, total funding of $338.9M)
- **Headquarters**: Tempe, Arizona, United States (global HQ; also Lysaker, Norway)
- **Founded**: 2016
- **Founders**: Not publicly available (co-founded by a team with industrial and tech backgrounds)

## Core Business
- **Primary industries**: Industrial AI, Industrial DataOps, Energy (oil & gas, renewables), Process Manufacturing, Power & Utilities
- **Target customers**: B2B – enterprise industrial companies (energy, manufacturing, chemicals, etc.)
- **Mission or purpose**: “Power a high-tech, sustainable, and profitable industrial future” — enabling industrial teams to move faster, work safer, and achieve more with AI.

## Products & Services
- **Cognite Data Fusion®**: Open Industrial DataOps platform that contextualizes operational, engineering, and IT data into a real-time Industrial Knowledge Graph, making data accessible for AI and analytics. (SaaS/platform)
- **Cognite Atlas AI™**: Low-code industrial AI agent workbench for building and deploying agentic AI to automate complex operational tasks. (AI agent platform)
- **Cognite Flows™**: Tool for building and scaling production-ready industrial workflows 100x faster. (Workflow automation)
- **Composable reference applications**: Pre-built solutions for production optimization, smart maintenance, workforce digitalization, and sustainability.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Total funding $338.9M (5 rounds); Annual revenue SEK 790.7M (approx. $75M based on current exchange rates, but exact USD not given)
- **Notable Investors/Partners**: Parent company Aker ASA; partners include Microsoft, Aker BP, Sopra Steria, Omny, Aize, and others. Recognized as a Leader in IDC MarketScape for Industrial DataOps Platforms.
- **Growth Signals**: 663 employees (+20.9% YoY); 102 active job postings (+67.2% YoY); LinkedIn followers 103,713 (+65.8% yearly); committed to delivering $100B in customer value by 2035; customers report 30-80% reduction in maintenance time and 4-month deployment cycles.

## Competitive Advantages
- **Industrial domain expertise**: Deep specialization in heavy industry (energy, process manufacturing) with a platform built for complex OT/IT data integration.
- **Open ecosystem**: Works with any cloud, any data source, any LLM, enabling flexible partnerships and off-the-shelf integrations.
- **Low-code AI agents**: Cognite Atlas AI allows domain experts to build AI agents without deep coding, speeding adoption.
- **Proven ROI**: Customers report 10x productivity improvements, rapid scaling (e.g., from pilot to 9 facilities in months), and significant downtime reduction.

## Strategic Focus
- **Scaling industrial AI**: Accelerate time-to-value with pre-built solutions, AI agents, and use-case best practices.
- **Global expansion**: Growing presence in Americas (Tempe, Austin, Houston), Europe (Oslo), Asia (Tokyo, Bengaluru).
- **Customer value commitment**: Ambitious goal of $100B in customer value by 2035.
- **Partner ecosystem**: Deepening integrations with cloud providers, system integrators, and industrial software vendors.

## Why Work Here
- **Culture**: Values of Relentless (innovation, learning from setbacks), Ownership (shared responsibility, inclusivity), and Impact (result-oriented, adding value to team and customers). Described as “smart, passionate builders, and problem solvers.”
- **Remote/Hybrid/Office**: Hybrid model with offices in Tempe, Austin, Houston, Oslo, Tokyo, Bengaluru. Some roles listed as “USA (remote)” indicating flexibility for certain positions.
- **Diversity**: Employees from 60+ countries; global team of engineers, product managers, domain experts.
- **Perks & Growth**: Opportunity to work on “biggest impact on our planet” — industrial AI at scale; strong investment in R&D and career development; fast-growing company with 100+ open roles.
- **Engineering culture**: Emphasis on AI, data engineering, machine learning, and full-stack development; roles like Senior AI Engineer, ML Engineer, Data Engineer, Applied AI Engineer.

## Sources
1. [cognite.com](https://www.cognite.com/en)
2. [cognite.com](https://www.cognite.com/en/company/about-us-cognite)
3. [cognite.com](https://www.cognite.com/en/company/careers)
4. [linkedin.com](https://www.linkedin.com/company/cognitedata)
5. [cbinsights.com](https://www.cbinsights.com/company/cognite)

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