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CoStar Group

Commercial real estate data, analytics, marketplaces

Senior MLOps Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSunnyvale, CA, USA

About the Job

Requirements

  • A bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field, or equivalent practical experience.
  • At least 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 and PyTorch, and optimization libraries.
  • A solid understanding of machine learning algorithms, model architectures, and deep learning concepts.
  • Experience with cloud platforms such as AWS, Azure, or GCP and deploying machine learning models in cloud environments.
  • Familiarity with version control systems such as Git and agile development methodologies.
  • Strong problem-solving skills and attention to detail, particularly in model performance and accuracy.
  • Strong verbal and written communication skills.

Responsibilities

  • Analyze and profile machine learning models to identify performance bottlenecks and areas for optimization.
  • Implement and apply model optimization techniques such as quantization, pruning, distillation, and neural architecture search to improve inference speed and reduce resource consumption.
  • Develop and integrate specialized libraries and tools for efficient model execution on various hardware platforms, including GPUs, CPUs, and edge devices.
  • Collaborate with machine learning research and development engineers to understand model architectures, training procedures, and deployment requirements.
  • Design and conduct experiments to measure the impact of optimization techniques on model performance and accuracy.
  • Automate model optimization workflows and build robust continuous integration and continuous deployment pipelines for optimized models.
  • Stay current with research and industry trends in machine learning model optimization, hardware acceleration, and efficient artificial intelligence.
  • Contribute to the continuous improvement of MLOps practices and infrastructure for model deployment and monitoring.
  • Ensure the scalability and reliability of optimized models in production environments.

Desired Qualifications

  • A master's degree in Computer Science or Data Science, or a related quantitative field.
  • At least 5 years of industry experience in machine learning model optimization, machine learning engineering, or MLOps, particularly with large-scale 2D or 3D computer vision models.
  • Experience with hardware-aware model optimization and deployment to edge devices.
  • Knowledge of model compression techniques and their practical application.
  • Experience with workflow orchestration tools such as Temporal, Airflow, or Kubeflow.
  • Familiarity with containerization technologies such as Docker and Kubernetes.
  • Demonstrated ability to build and maintain robust, scalable, and automated machine learning model deployment pipelines.
  • Experience working in a fast-paced research and development environment.
  • Excellent written and verbal communication skills, with the ability to articulate complex technical concepts to diverse audiences.

About the Company

CoStar Group

Commercial real estate data, analytics, marketplaces

CoStar Group provides information, analytics, and online marketplaces for the commercial real estate industry. Its core products are detailed property data, market analyses, and tools to compare and evaluate properties, delivered via subscription access. It also runs online marketplaces like Apartments.com and LoopNet that connect renters with apartments and buyers/tenants with commercial properties, earning revenue from subscriptions, advertising, and transaction fees. The company differentiates itself through a large, integrated data platform and a suite of marketplaces that cover both residential rentals and commercial properties, with a strong presence in the U.S. and expanding globally. Its goal is to help brokers, property owners, investors, and lenders make informed real estate decisions and efficiently connect buyers, renters, and property listings across markets.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Washington DC, District of Columbia
Founded
1987

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