About the job
As a Machine Learning Infrastructure Engineer, you’ll build scalable, reliable, and high-performance infrastructure that powers ML systems across our organization. You’ll operate at the scales of hundreds of billions of engagements, and redefine how we deliver performance ads to hundreds of millions of users.
Responsibilities
Lead strategic planning and roadmap execution of scalable production-ready ML systems including model training, data pipelines, feature engineering and model inference.
Own the architecture, establish engineering best practices of scalability, reliability, and cost-effectiveness of ML infrastructure (e.g., training, serving, feature).
Work closely with data scientists, ML engineers, platform teams, and product stakeholders to design, implement, and operate robust ML platforms that accelerate model development and deployment.
Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading-edge technologies and practices.
Qualifications
Minimum
5+ years of experience designing, building, and deploying large-scale machine learning systems in production environments.
3+ years of experience tech leading ML infrastructure engineers
Strong communication skills and a collaborative approach to problem solving.
BS, MS, or Ph.D. in Computer Science, Engineering, or equivalent experience.
Preferred
No preferred qualifications listed.