Senior Machine Learning Engineer, Computer Vision at Metropolis

Seattle, Washington, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, TechnologyIndustries

Requirements

M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field
5+ years of industry experience in computer vision, image retrieval, or perception systems
Strong background in object detection, tracking, and scene understanding using multi-camera inputs
Deep understanding of vector-based retrieval systems and experience with OpenSearch, FAISS, or similar tools
Proficiency in Python or C++, with hands-on experience in PyTorch, TensorFlow, and OpenCV
Experience in building large-scale image retrieval pipelines, including feature extraction, indexing, and search optimization
Knowledge of multi-view geometry, and cross-camera identity association
Experience evaluating and tuning re-ranking strategies using contextual and multi-modal signals
Exposure to cloud-based deployment of search systems (e.g., OpenSearch cluster tuning, sharding, replication)
Experience with edge deployment of perception pipelines (e.g., Jetson, Qualcomm)
Publications or patents in the fields of visual search, localization, or multi-camera perception

Responsibilities

Design and implement algorithms for multi-camera object detection, classification, and persistent tracking
Build scene understanding modules to extract landmarks, spatial layout, and semantic context from image streams
Develop cross-camera fusion and localization methods for consistent identification and positioning of objects
Architect and deploy visual search systems using vector databases (e.g., OpenSearch, FAISS, Milvus) for image-based retrieval and matching
Design and implement re-ranking techniques to improve retrieval precision based on context, metadata, and scene cues
Create tools and metrics to evaluate retrieval quality, localization accuracy, and perception robustness
Collaborate across ML, backend, and infrastructure teams to ensure scalable, real-time deployment
Investigate system-level issues, drive debugging efforts, and improve model and system performance
Mentor junior engineers and contribute to long-term vision for perception, localization, and image retrieval

Skills

Key technologies and capabilities for this role

Computer VisionObject DetectionObject TrackingScene UnderstandingLocalizationMulti-Camera FusionVector DatabasesFAISSMilvusOpenSearchRe-rankingImage Retrieval

Questions & Answers

Common questions about this position

What qualifications are required for the Senior Machine Learning Engineer role?

Candidates need an M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related field, plus 5+ years of industry experience in computer vision, image retrieval, or perception systems.

What technical skills are essential for this position?

Key skills include strong background in object detection, tracking, and scene understanding with multi-camera inputs, deep understanding of vector-based retrieval systems like OpenSearch or FAISS, and proficiency in Python or C++, PyTorch, TensorFlow, and OpenCV.

What is the salary range for this role?

This information is not specified in the job description.

Is this position remote or does it require office work?

This information is not specified in the job description.

What experience stands out for a strong application?

Highlight 5+ years in computer vision or perception systems, experience building large-scale image retrieval pipelines with vector databases like FAISS, and expertise in multi-camera tracking and scene understanding to demonstrate fit for leading advanced perception systems.

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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