Senior Machine Learning Engineer, Teads

• We are seeking a highly skilled ML Engineer with a strong focus on backend development, data engineering, and infrastructure management
• 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

Desirable

• 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
• 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
• 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