Staff Computer Vision Engineer, Perception at Metropolis

Seattle, Washington, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Computer VisionIndustries

Requirements

M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or related field with a focus on computer vision or machine learning
8+ years of industry experience in developing and deploying perception algorithms in real-world applications
Strong proficiency in modern deep learning frameworks (e.g., PyTorch)

Responsibilities

Architect and develop advanced perception algorithms for tasks such as object detection, tracking, semantic segmentation, and scene understanding
Take initiative to identify gaps, bottlenecks, and opportunities in perception performance and system design—then drive solutions from concept through deployment
Define and implement key metrics and KPIs to rigorously evaluate perception system accuracy, robustness, and scalability across diverse real-world conditions
Drive system-level debugging and root cause analysis for perception failures in production environments
Collaborate with hardware, ML, and software engineering teams to improve data pipelines, model deployment frameworks, and sensor configurations
Mentor and lead by example, promoting best practices in scalable architecture, model evaluation, and engineering excellence within the computer vision team
Research, prototype, and integrate new machine learning or deep learning approaches applicable to perception problems
Participate in cross-team initiatives to improve system reliability, labeling quality, simulation-based validation, optimizing perception models and production-readiness of perception models
Stay ahead of the curve by researching, prototyping, and integrating emerging deep learning techniques and tools into our perception stack

Skills

Computer Vision

Perception Algorithms

Object Detection

Object Tracking

Recognition

Video Analytics

Multi-Modal Systems

Machine Learning

Edge Deployment

Cloud Platforms

Metropolis

Technology-driven facility management for parking

About Metropolis

Metropolis focuses on improving facility management, particularly in the parking sector, by using computer vision technology to facilitate checkout-free payments. This technology allows drivers to park and pay without the need for traditional payment methods, making the process more efficient. Metropolis serves a variety of clients, including real estate owners and valet services, and operates in over 360 cities across North America, processing more than $4 billion in payments each year. What sets Metropolis apart from its competitors is its integration of payment solutions with facility management services, which not only enhances the parking experience but also increases asset productivity for its clients. The company's goal is to streamline parking operations and create revenue opportunities for its clients through its advanced technology and services.

Santa Monica, CaliforniaHeadquarters

2017Year Founded

$1,240.7MTotal Funding

DEBTCompany Stage

Automotive & Transportation, Fintech, Real EstateIndustries

501-1,000Employees

Benefits

Health Insurance

401(k) Retirement Plan

Disability Insurance

Life Insurance

Stock Options

Performance Bonus

Risks

Increased competition from BMW-owned ParkMobile may impact market share.

The SP Plus acquisition may pose integration challenges and financial strain.

Rapid expansion could lead to operational inefficiencies and quality control issues.

Differentiation

Metropolis uses AI and computer vision for checkout-free parking payments.

The company integrates its platform into field operations for revenue generation.

Metropolis serves diverse clients, including real estate and valet services.

Upsides

Metropolis processes over $4 billion in payments annually across 360 cities.

The acquisition of SP Plus Corporation expands Metropolis's market reach.

AI-driven dynamic pricing models optimize revenue in real-time.

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