Matterport - Senior ML Ops Engineer

CoStar Group

Hybrid

Quick summary

Work type: Hybrid
Location: Sunnyvale, CA
Salary: $173,000–$253,000 / yr
Posted: 3 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $185k

This role $213k

$133k most similar roles pay here $266k

This role pays more than 69% of similar roles. Most pay $149,175–$220,312 — the shaded band above. At the midpoint, this role pays about $213k versus about $185k for comparable roles.

Based on 239 similar postings.

Employer

About CoStar Group

CoStar Group is the leading provider of commercial real estate information, analytics, and online marketplaces, including CoStar, Apartments.com, and LoopNet platforms. Industry: Commercial Real Estate Data & Analytics

CoStar Group currently has 36 open roles on FindRole.

Listed pay typically runs $146,500–$202,000 across 16 roles with salary data.

Most-posted roles

View all roles at CoStar Group

At a glance

TL;DR · Matterport - Senior ML Ops Engineer

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As a Senior MLOps Engineer at Matterport, you will join the CoStar Group’s cutting-edge team to enhance the performance and scalability of machine learning models used in spatial computing. Your daily tasks include analyzing model bottlenecks, applying optimization techniques like quantization and pruning, and developing efficient execution tools for diverse hardware platforms. You’ll collaborate closely with ML R&D Engineers to ensure seamless integration of optimized models into production environments, focusing on improving inference speed and resource utilization while maintaining accuracy. This role demands expertise in Python, machine learning frameworks such as TensorFlow or PyTorch, cloud platforms like AWS or GCP, and familiarity with CI/CD pipelines and version control systems. Ideal candidates have a background in large-scale 2D/3D computer vision models and experience with workflow orchestration tools and containerization technologies.

Skills

Python TensorFlow PyTorch AWS Azure GCP Git CI/CD Docker Kubernetes Temporal Airflow Kubeflow Prometheus PostgreSQL

What you'll do

Analyze and profile ML models to identify performance bottlenecks.
Implement model optimization techniques like quantization and pruning.
Develop specialized libraries for efficient model execution on hardware platforms.
Design experiments to measure the impact of optimization techniques.
Automate model optimization workflows and build CI/CD pipelines.
Stay updated with latest trends in ML model optimization and hardware acceleration.
Ensure scalability and reliability of optimized models in production environments.

What we're looking for

3+ years of machine learning engineering experience focusing on model optimization and deployment.
Proficiency in Python with strong programming skills and experience using TensorFlow or PyTorch.
Solid understanding of machine learning algorithms, deep learning concepts, and model architectures.
Experience deploying ML models in cloud environments like AWS, Azure, or GCP.
Familiarity with version control systems such as Git and agile development methodologies.
Excellent problem-solving skills for optimizing model performance and accuracy.

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