Senior Manager, Machine Learning Engineering

Who We Are

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy – a future where mundane repetition disappears and being known unlocks access, comfort and belonging everywhere you go. From transforming parking into a seamless drive‑in, drive‑out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical.

The future isn’t coming; it’s here, and we need builders, innovators and problem solvers to help us create it.

Metropolis is seeking a Senior Manager of Machine Learning Engineering within the Advanced Technologies Group to lead the technical vision and execution of our foundational systems that power our next generation of AI. You will oversee 4 critical pillars within the Machine Learning org: data engineering, annotation pipelines, ML Infrastructure and Deployment of Agentic AI solutions. You are a hands‑on, senior technical leader with a broad dynamic range, capable of providing high‑level strategic direction while remaining technically proficient enough to dive into the weeds with your team.

Your mission is to transition state‑of‑the‑art models into robust, autonomous production systems that automate complex enterprise workflows, partnering closely with internal engineering teams and external vendors to build the scalable tools and data pipelines that define the future of the recognition economy.

• Build and maintain scalable, compliant and auditable data infrastructure to serve computer vision and AI pricing use cases
• Build scalable data engineering pipelines and automated annotation workflows (LLM‑in‑the‑loop) to reduce reliance on manual labeling and accelerate model iteration
• Own the MLOps lifecycle, including distributed training infrastructure, model registries, and low‑latency inference services. Ensure high availability and observability for all deployed models
• Define technical direction, lead and grow a high‑performance team of data and ML infrastructure engineers to influence impactful business outcomes
• Develop foundational systems to product ionize agentic AI, Large Language Models (LLMs) and Vision Language Models (VLMs) solutions for workflow automation to enhance our products
• Enable Metropolis’s move into personalization and targeted advertisement through innovative ML data pipelines and feature stores
• Collaborate with external vendors and annotation platform providers to ensure high‑quality data for production models
• Partner with other ML leaders (Growth, Edge deployment) and cross‑functional leaders in Hardware, Platform, and Product engineering to align development roadmaps

• 10+ years of professional experience in data and machine learning engineering with proven expertise in building enterprise‑scale, auditable ETL pipelines and data governance mechanisms
• 5+ years of experience in leadership and management, ideally having managed other managers
• MS or PhD in computer science and/or a quantitative discipline
• Strong experience in distributed data processing like Apache Spark, Kafka, Cloud native data storage and processing services
• 1+ year experience building data/eval pipelines and deploying agentic AI solutions (LLMs and/or VLMs)
• Experience managing technical programs, defining milestones, and communicating progress to diverse audiences
• Familiarity with deep learning frameworks such as Tensor Flow or Py Torch
• Strong proficiency with SQL and Python
• Engage effectively with external data providers and vendors
• Familiarity with computer vision systems and models (e.g. object detection, tracking, segmentation)

• Manage large scale datasets and database tools for data processing
• Deploy ML services to the cloud with a focus on scalability and reliability
• Operate in innovative, high‑growth environments

Metropolis values in‑person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office‑first model, which requires…