Time since we first saw it
Day 8
Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
Machine Learning Engineer
India (Bengaluru)
Actively hiring, first seen by us 8 days ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingAs a Machine Learning Engineer, you will be a key contributor to building the models and pipelines that power our industrial contextualization platform.
You love writing clean, readable code and seeing it work.
Build, test, and maintain data pipelines for large-scale industrial datasets
Assist in deploying data science prototypes to production
Write Python APIs (RESTful/gRPC) for ML capabilities
Containerize applications with Docker and handle unstructured industrial data on Linux
Run experiments and fine-tune foundational NLP and vision-language models
From the employer’s posting
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.
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.
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.
Education & alternatives
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.
About Cognite - AI for Industry
We are building the next generation of contextual AI for Industrial Operations.
In the employer’s words · Read in context
Posting record
Report an errorActively hiringFirst seen by us 8 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 27, 20268 days ago
- Recorded sightings
- 10
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Cognite - AI for Industry is hiring
7 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring at a steady pace
- Takes jobs down at a usual pace
- 29% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- Delta Lake
- Docker
- GCP
- Iceberg
- Dask
- FastAPI
- Flask
- Hugging Face
- pandas
- PySpark
- PyTorch
- TensorFlow
Where the posting names them
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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
- 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)
Your next step
Complete your application on job-boards.eu.greenhouse.io. The employer’s form will show what is required.
Applying through Greenhouse
- No account needed: the form is one page, with an “Autofill my application” option that reads your resume.
- Greenhouse doesn’t read resumes over 2.5 MB, or details placed in a header, footer, text box, table or second column. The file still attaches, but a recruiter has to type you in by hand.
Already applied? Track this application
Has Cognite - AI for Industry posted this job before?
Yes. We’ve followed one other Machine Learning Engineer job there since May 2026. It came down after 4 days.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
What Cognite is: Relentless to achieve
About the Opportunity
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 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.
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
- 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.
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.
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- Location & working pattern
India (Bengaluru)
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