Senior Staff MLOps Engineer (H/F)

Posted 2 Days Ago
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Paris, Île-de-France
Senior level
AdTech • Big Data • Digital Media • Marketing Tech
IAS is a global media measurement and optimization platform.
The Role
The Senior Staff MLOps Engineer will design and implement scalable data pipelines and production systems for machine learning. Responsibilities include developing tools for machine learning training and inference, working with various stakeholders, applying software engineering best practices, and testing/data science model validation.
Summary Generated by Built In

Integral Ad Science (IAS) is a global technology and ad measurement company that builds verification, optimization, and analytics solutions for the advertising industry, and we are looking for a Senior Staff Machine Learning Operations Engineer to join our Research and Development (R&D) team. If you are excited by technology that has the power to handle hundreds of thousands of transactions per second; collect tens of billions of events each day; and evaluate thousands of data-points in real-time all while responding in just a few milliseconds, then IAS is the place for you! 

As a Senior Staff MLOps Engineer, you will work within the Data Science and Machine Learning team to design and implement data pipelines, and build and run models into large scale production systems. This role is ideal for engineers who enjoy working in a collaborative and agile environment and solving complex problems using innovative solutions with a desire to improve the status quo.

What you'll get to do:

  • Develop and deploy scalable tools and services to handle machine learning training and inference
  • Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
  • Facilitate the development and deployment of proof-of-concept machine learning systems
  • Communicate with machine learning researchers, data scientists, data engineers, and architect and document the processes
  • Design the data pipelines and engineering infrastructure to support our machine learning systems at scale
  • Data science models testing, validation, and tests automation

You should apply if you have most of this:

  • 7+ years experience in a backend/microservices developer role in an Agile environment
  • Strong software engineering skills in complex multi language systems
  • Knowledge of Machine Learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, ...)
  • Ability to understand tools used by data scientists
  • Experience in using popular MLOps frameworks like MLFlow, Kubeflow, …
  • Experience building custom integration between cloud based systems
  • Experience developing with containers and Kubernetes in cloud computing environments
  • Experience with writing unit and integration tests for the application code you work on
  • Basic knowledge of RESTful systems, Object-Oriented design, Design Patterns, Data Structures and Algorithms

What puts you over the top:

  • Experience with Databricks
  • Strong communication skills, to be able to articulate solutions to other team members and even non-technical people
  • Experience with GPU training and inference
  • Problem solver, team player, lifelong learner

About Integral Ad Science 

Integral Ad Science (IAS) is a leading global media measurement and optimization platform that delivers the industry’s most actionable data to drive superior results for the world’s largest advertisers, publishers, and media platforms. IAS’s software provides comprehensive and enriched data that ensures ads are seen by real people in safe and suitable environments, while improving return on ad spend for advertisers and yield for publishers. Our mission is to be the global benchmark for trust and transparency in digital media quality. For more information, visit integralads.com.

Equal Opportunity Employer:

IAS is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities and veterans to apply.

California Applicant Pre-Collection Notice:

We collect personal information (PI) from you in connection with your application for employment or engagement with IAS, including the following categories of PI: identifiers, personal records, commercial information, professional or employment or engagement information, non-public education records, and inferences drawn from your PI. We collect your PI for our purposes, including performing services and operations related to your potential employment or engagement. For additional details or if you have questions, contact us at [email protected].

To learn more about us, please visit http://integralads.com/ 

Attention agency/3rd party recruiters: IAS does not accept any unsolicited resumes or candidate profiles. If you are interested in becoming an IAS recruiting partner, please send an email introducing your company to [email protected]. We will get back to you if there's interest in a partnership.

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Top Skills

Python

What the Team is Saying

Easton
Caitlin
Jessica
Timothy
Zach
Lisa
Milap
The Company
HQ: New York, NY
900 Employees
Hybrid Workplace
Year Founded: 2009

What We Do

Integral Ad Science (IAS) is a leading global media measurement and optimization platform that delivers the industry’s most actionable data to drive superior results for the world’s largest advertisers, publishers, and media platforms. IAS’s software provides comprehensive and enriched data that ensures ads are seen by real people in safe and suitable environments, while improving return on ad spend for advertisers and yield for publishers. Our mission is to be the global benchmark for trust and transparency in digital media quality. For more information, visit integralads.com.

Why Work With Us

The IAS media measurement and optimization platform delivers the most actionable data to drive superior results.

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