Senior Manager, Machine Learning Engineering

About the role

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.

Responsibilities

• 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 productionize 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

Requirements

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+ years 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 TensorFlow or PyTorch

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)

Qualifications

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

Skills

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

Benefits

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 employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection

Pay

$200,000.00 USD to $250,000.00 USD annually

Schedule

4 Days in Office