Senior Machine Learning Engineer

People In AI is a rapidly scaling AI startup building machine learning infrastructure that transforms massive behavioral datasets into highly predictive, production-ready intelligence for enterprise customers. They are seeking multiple Senior Machine Learning Engineers to tackle deeply technical challenges in personalization and representation learning while working with large-scale datasets. The role involves designing and deploying production ML systems and collaborating closely with various teams to bring ML systems into production.

Responsibilities:

• Design and deploy production ML systems focused on personalization, recommendation, ranking, and predictive modeling
• Build representation learning systems that transform large-scale behavioral data into compact, high-signal embeddings
• Develop and optimize recommendation engines, CTR prediction models, sequence models, and ad optimization systems
• Create scalable ML pipelines and infrastructure that support experimentation, deployment, monitoring, and continuous improvement
• Work with large-scale, consented datasets spanning purchasing activity, browsing behavior, app usage, location signals, and more
• Contribute to privacy-focused machine learning systems and secure data processing workflows
• Partner closely with engineering, product, infrastructure, and research teams to bring ML systems into production
• Help shape ML architecture, tooling, and best practices within a fast-moving startup environment

Requirements:

• Proven experience shipping machine learning systems into production environments
• Strong Python skills and hands-on experience with modern ML frameworks
• Experience working on personalization, recommendation systems, ranking models, embeddings, or representation learning problems
• Familiarity with large-scale behavioral datasets and production ML infrastructure
• Strong understanding of experimentation, model evaluation, feature engineering, and scalable deployment practices
• Experience with orchestration and infrastructure tooling such as Airflow, Spark, CI/CD systems, or similar technologies
• Strong communication and collaboration skills across technical and product teams
• Comfort operating in fast-moving startup environments with high ownership and ambiguity