Senior ML Ops Engineer

CoStar Group

Confirmed live 2 days ago High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Sunnyvale, CA
Salary: $173,000–$253,000 / yr
Posted: 91 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $204k

This role $213k

$151k most similar roles pay here $264k

This role pays more than 51% of similar roles. Most pay $162,000–$246,150 — the shaded band above. At the midpoint, this role pays about $213k versus about $204k for comparable roles.

Based on 240 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 65 open roles on FindRole.

Listed pay typically runs $133,000–$190,000 across 63 roles with salary data.

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At a glance

TL;DR · Senior ML Ops Engineer

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As a Matterport - Senior ML Ops Engineer, you will join the engineering team to enhance the performance, efficiency, and scalability of machine learning models within a spatial computing platform. You will be responsible for identifying bottlenecks, applying optimization techniques like quantization, pruning, distillation, and neural architecture search, and deploying high-performing models into production. Collaborating with ML R&D Engineers, you will develop specialized libraries for execution on GPUs, CPUs, and edge devices while building automated CI/CD pipelines. The role requires proficiency in Python, experience with TensorFlow or PyTorch, and familiarity with cloud platforms like AWS, Azure, or GCP. You will also utilize tools such as Git, Docker, Kubernetes, and workflow orchestrators like Temporal or Airflow to ensure robust model deployment for 2D and 3D computer vision applications.

Skills

MLOps Python TensorFlow PyTorch Quantization Pruning Distillation Neural Architecture Search CI/CD AWS Azure GCP Git Docker Kubernetes Airflow Temporal Kubeflow Computer Vision

What you'll do

Analyze and profile machine learning models to identify performance bottlenecks and optimization opportunities.
Implement model optimization techniques including quantization, pruning, distillation, and neural architecture search.
Develop and integrate specialized libraries for efficient model execution on GPUs, CPUs, and edge devices.
Design and conduct experiments to measure the impact of optimizations on model accuracy and speed.
Automate model optimization workflows and build robust CI/CD pipelines for production deployment.
Ensure the scalability and reliability of optimized models within the spatial computing platform.
Improve MLOps infrastructure for continuous monitoring and deployment of machine learning models.

What we're looking for

Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field.
3+ years of experience in machine learning engineering with a focus on model optimization and deployment.
Proficiency in Python and strong programming skills.
Experience with machine learning frameworks such as TensorFlow or PyTorch and associated optimization libraries.
Experience deploying ML models in cloud environments using platforms like AWS, Azure, or GCP.
Familiarity with version control systems like Git and agile development methodologies.
Knowledge of model compression techniques including quantization, pruning, distillation, and neural architecture search.
Experience with containerization technologies such as Docker and Kubernetes.