Senior Manager, Machine Learning Engineering

Responsibilities

• Build and maintain scalable, compliant, and auditable data infrastructure for computer vision and AI pricing
• Develop scalable data engineering pipelines and automated annotation workflows using LLM-in-the-loop
• Own the MLOps lifecycle, including distributed training, model registries, and low-latency inference services
• Lead and grow a high-performance team of data and ML infrastructure engineers
• Develop foundational systems to productionize agentic AI, LLMs, and VLMs for workflow automation
• Enable personalization and targeted advertisement through innovative ML data pipelines and feature stores
• Collaborate with external vendors and cross-functional leaders in Hardware, Platform, and Product engineering

Requirements

• 10+ years of professional experience in data and machine learning engineering
• 5+ years of experience in leadership and management, ideally managing other managers
• MS or PhD in computer science or a quantitative discipline
• Strong experience in distributed data processing (Apache Spark, Kafka, Cloud native services)
• 1+ years experience building data/eval pipelines and deploying agentic AI solutions (LLMs/VLMs)
• Proficiency with SQL and Python
• Familiarity with deep learning frameworks (TensorFlow or PyTorch)
• Familiarity with computer vision systems (object detection, tracking, segmentation)

Preferred Qualifications

• Experience managing large scale datasets and database tools for data processing
• Experience deploying ML services to the cloud with a focus on scalability and reliability
• Experience operating in innovative, high-growth environments

About the Company

Metropolis is creating the artificial intelligence to make the real world responsive, pioneering the Recognition Economy to transform parking and expand intelligence across retail and hospitality.