Director of ML Engineering & Infrastructure

Requirements • 10+ years of industry experience spanning machine learning engineering and distributed systems, • 3+ years of leadership and management experience, with a proven ability to build and lead strong technical teams, • MSc or Ph.D. in Computer Science, Machine Learning, or related field, or equivalent practical experience, • Proven expertise in building and deploying end-to-end ML systems at scale, including recommendation and personalization systems, • Strong background in distributed systems architecture, including low-latency services, streaming platforms, and large-scale serving, • Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and ML infrastructure technologies, • Track record of delivering high-quality, scalable, and fault-tolerant systems, • Excellent communication skills and ability to influence product and technical strategy, • Proven experience deploying large-scale serving systems on AWS and demonstrated expertise in leveraging Databricks for large-scale data processing and ML workflows What the job involves • We are seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging advanced ML engineering with world-class infrastructure design, • In this role, you will own the strategic direction and execution for scaling our machine learning capabilities while ensuring our distributed systems and infrastructure can support innovation at massive scale, • You will combine technical depth with leadership excellence to guide teams that deliver both foundational ML systems and high-performance distributed services, • Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth, • Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization, • Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving, • Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence, • Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences, • Support best practices in experimentation, evaluation, and ML system monitoring, • Ensure cost efficiency, scalability, and performance in ML infrastructure investments