Staff Machine Learning Engineer

Job Description: Integral Ad Science (IAS) is a global technology and data company that builds verification, optimization, and analytics solutions for the advertising industry, and we’re looking for a Staff Machine Learning Engineer on the Data Science 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 Staff Machine Learning Engineer at IAS, you will be part of a team that is at the center of innovation for the company and a major contributor to our core products. You will oversee a sophisticated suite of data science systems making large-scale business predictions across the open web, social networks, video, and mobile apps. At the Staff level, you are expected to be a technical pillar for the organization. You will take ownership of open, highly ambiguous business problems and translate them into scalable ML architectures. You will define technical roadmaps, set the standard for ML engineering practices, and push the boundaries of applied machine learning to deliver best-in-class solutions for our clients. Innovation is at the heart of our competitive advantage, and you will cultivate it by mentoring talent and raising the technical bar across multiple teams.The types of challenges we solve have attracted people from industry and academia with diverse backgrounds. We’re passionate about maintaining an open and collaborative environment, where team members bring their own unique style of thinking and tools to the table. What you’ll get to do: Technical Leadership & Vision: Drive the architectural vision and system design for our core AI/ML-based services. Act as the technical lead for complex, multi-quarter initiatives from inception to global deployment. Architect at Scale: Design and build large-scale deep learning infrastructure and platforms for distributed model training, ensuring low-latency and high-availability at enterprise scale. Cross-Functional Influence: Partner with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy. Translate abstract product requirements into concrete technical designs. Act as a Multiplier: Mentor and guide senior and mid-level data scientists and engineers. Establish standard methodologies, define code quality expectations, and lead architecture design reviews. Infrastructure Mastery: Work with large-scale AI training infra components (accelerators, network fabrics, CUDA, NCCL, RDMA) and big data ecosystems (Databricks, Spark, Kubernetes, Kafka, Prometheus). End-to-End Ownership: Design, develop, and support robust CI/CD pipelines for AI/ML services, ensuring models transition smoothly from research into reliable production endpoints. You should apply if you have most of this experience: …