Senior Machine Learning Engineer

People In AI is a well-capitalized AI company focused on building large-scale behavioral modeling systems. They are seeking a Senior Machine Learning Engineer to turn foundational behavioral models into scalable applications and drive measurable impact through real-world ML systems operating at scale.

Responsibilities:

• Turn foundational behavioral models into scalable, revenue-driving applications
• Build and iterate on ML-powered personalization and prediction systems in production
• Close the loop between representation learning, experimentation, and product outcomes
• Deliver measurable impact through real-world ML systems operating at scale
• Own models, metrics, pipelines, and deployment for ML-powered products
• Collaborate closely with Product, Engineering, and foundational ML teams
• Drive 0-to-1 product initiatives as well as iterative optimization
• Operate across the full ML lifecycle from data to inference to monitoring
• Design and deploy recommender, ranking, or personalization models in production
• Build and maintain data processing and training pipelines
• Define evaluation metrics tied directly to business outcomes
• Partner with product stakeholders to shape experimentation strategy
• Improve system performance through monitoring, iteration, and feedback loops
• Contribute to infrastructure and tooling that support scalable ML workflows

Requirements:

• Experience shipping ML systems in production environments
• Background in recommender systems, personalization, CTR modeling, ranking, or related domains
• Comfort working across the full ML stack, including data orchestration and deployment
• Strong product instincts and ability to translate model performance into business value
• Pragmatic problem-solving mindset with a bias toward impact
• Comfort operating in a lean, fast-moving environment
• Design and deploy recommender, ranking, or personalization models in production
• Build and maintain data processing and training pipelines
• Define evaluation metrics tied directly to business outcomes
• Partner with product stakeholders to shape experimentation strategy
• Improve system performance through monitoring, iteration, and feedback loops
• Contribute to infrastructure and tooling that support scalable ML workflows
• Python and large-scale ML frameworks
• Distributed data processing systems
• Workflow orchestration and CI/CD tooling
• Experiment tracking and evaluation systems
• Production inference services