Job Specifications
About Teads
Teads is the omnichannel outcomes platform for the open internet, driving full-funnel results for marketers across premium media. With a focus on meaningful business outcomes for branding and performance objectives, the combined company ensures value is driven with every media dollar by leveraging predictive AI technology to connect quality media, beautiful brand creative, and context-driven addressability and measurement. One of the most scaled advertising platforms on the open internet, Teads is directly partnered with more than 10,000 publishers and 20,000 advertisers globally. The company is headquartered in New York, with a global team of nearly 1,800 people in 30+ countries.
For more information, visit www.teads.com.
About The Opportunity
We are seeking a highly skilled ML Engineer with a strong focus on backend development, data engineering, and infrastructure management. In this role, you will tackle complex challenges at the intersection of backend, DevOps, and data engineering to build and optimize the infrastructure supporting machine learning models and large-scale data workflows.
You will be responsible for designing, developing, and optimizing ML flows, creating robust data pipelines, and addressing infrastructure challenges to ensure the scalability, performance, and efficiency of our systems. Collaboration with data scientists and cross-functional teams will be key to delivering production-ready solutions that drive impactful business outcomes.
The position is based in Paris, and we have a hybrid model: 3 days in the office, 2 days remote.
What will you do?
As a Senior Machine Learning Engineer, your mission will be:
Design, develop, and optimize backend systems for scalable and efficient data processing, particularly in machine learning workflows.
Develop and maintain ML flows to streamline data preprocessing, model training, evaluation, and deployment, ensuring smooth integration between systems.
Build and optimize data pipelines using Spark, Airflow, BigQuery, and AWS S3 for handling large-scale data ingestion, transformation, and processing.
Manage AWS Batch for large-scale distributed training jobs, optimizing compute resources and cost management for ML workflows.
Solve complex infrastructure challenges, focusing on performance and low-latency systems that support ML and data-heavy applications.
Collaborate with data scientists, DevOps engineers, and other backend engineers to design end-to-end solutions, integrating ML models with the infrastructure.
Manage cloud-based infrastructure (AWS, GCP), focusing on cost optimization, security, and scalability for data and ML systems.
Ensure that all backend systems are tested, optimized, and continuously monitored, including writing, running, and automating unit, functional, and load tests.
Stay up-to-date with emerging technologies and contribute to the ongoing improvement of backend systems, ML workflows, and data engineering tools.
Experience with performance engineering, profiling, and optimizing systems for high efficiency and low latency.
What You Will Bring To The Team
Backend development expertise in Python, Java, and Scala, with a strong understanding of software engineering practices, debugging, and performance optimization.
Experience building and optimizing ML flows, integrating model training, data preprocessing, and deployment pipelines.
Hands-on experience with data engineering tools like Spark, Airflow, BigQuery, and AWS S3, as well as building large-scale data pipelines.
Proficiency with AWS Batch for managing large-scale compute resources and distributed ML training.
Familiarity with cloud infrastructures (AWS, GCP), and expertise in managing scalable backend services and data pipelines in production.
DevOps skills, including experience with CI/CD pipelines, Terraform, Docker, and Kubernetes to automate and manage infrastructure.
Strong problem-solving abilities to address complex backend and infrastructure issues related to performance, scalability, and efficiency.
Excellent communication skills for working cross-functionally, explaining complex concepts clearly to technical and non-technical teams.
Bonus Points For Experience In
Understanding data science principles and machine learning concepts.
Familiarity with ML frameworks such as TensorFlow or PyTorch.
The Team
The AI department currently consists of 50 people who are a mix of data scientists, machine learning and backend engineers
The department provides technologies that power outcomes of campaigns with a total yearly turn over of $1.7B
Runs large scale prediction and control systems for ad delivery, dealing with millions of live ads, doing more than a billion predictions per second based on large on-line trained models being updated every 5 minutes
Why Join Our Team?
Impact: Your work will directly empower hundreds of engineers and significantly impact Teads' ability to innovate and deliver.
Challenge: You'll tackle complex, high-scale