Job description

Matterport - Senior ML Ops Engineer Job Description CoStar Group is a leading global provider of commercial and residential real estate information, analytics, and online marketplaces. Included in the S&P 500 Index, CoStar Group is on a mission to digitize the world’s real estate, empowering all people to discover properties, insights and connections that improve their businesses and lives. We have been living and breathing the world of real estate information and online marketplaces for over 35 years, giving us the perspective to create truly unique and valuable offerings to our customers. We’ve continually refined, transformed, and perfected our approach to our business, creating a language that has become standard in our industry, for our customers, and even our competitors. We continue that effort today and are always working to improve and drive innovation. This is how we deliver for our customers, our employees, and investors. By equipping the brightest minds with the best resources available, we provide an invaluable edge in real estate. About Matterport: Matterport is leading the digital transformation of the built world. Our groundbreaking spatial computing platform turns buildings into data making every space more valuable and accessible. Millions of buildings in more than 170 countries have been transformed into immersive Matterport digital twins to improve every part of the building lifecycle from planning, construction, and operations to documentation, appraisal, and marketing. About the Role: As a Senior MLOps Engineer at Matterport, a part of CoStar Group, you will be pivotal in enhancing the performance, efficiency, and scalability of our machine learning models. You will be responsible for identifying bottlenecks, applying advanced optimization techniques, and deploying highly efficient models into production. You will work closely with ML R&D Engineers and other engineering teams to analyze model performance, optimize inference speed and resource utilization, and ensure the seamless integration of optimized models into our spatial computing platform. This role requires a strong understanding of machine learning principles, expertise in model optimization techniques, and a passion for pushing the boundaries of what s possible with efficient ML deployment. You will contribute to a product that is revolutionizing how people interact with and understand real estate by ensuring our models are robust, fast, and deliver exceptional user experiences. This position is located in Sunnyvale, CA and offers a schedule of 4 days on-site and 1 day work from home. What you will do: 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 (e.g., GPUs, CPUs, edge devices). Collaborate with ML R&D 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/continuous deployment (CI/CD) pipelines for optimized models. Stay up-to-date with the latest research and industry trends in ML model optimization, hardware acceleration, and efficient AI. 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. Basic Qualifications: Bachelor s degree in Computer Science, Data Science, Engineering, or a related quantitative field, or equivalent practical experience. 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 (e.g., TensorFlow, PyTorch) and optimization libraries. Solid understanding of machine learning algorithms, model architectures, and deep learning concepts. Experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments. Familiarity with version control systems (e.g., Git) and agile development methodologies. Excellent problem-solving skills and attention to detail, particularly in model performance and accuracy. Strong verbal and written communication skills. Preferred Qualifications: Master s degree in Computer Science, Data Science, or a related quantitative field. 5 years of industry experience in ML Model Optimization, ML Engineering, or MLOps, particularly with large-scale 2D/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 (e.g. Temporal, Airflow, Kubeflow). Familiarity with containerization technologies (e.g., Docker, Kubernetes). Demonstrated ability to build and maintain robust, scalable, and automated ML model deployment pipelines. Experience working in a fast-paced R&D environment. Excellent communication skills, both written and verbal, with the ability to articulate complex technical concepts to diverse audiences. Perks & Benefits: When you join CoStar Group, you’ll experience a collaborative and innovative culture working alongside the best and brightest to empower our people and customers to succeed. We offer you generous compensation and performance-based incentives. CoStar Group also invests in your professional and academic growth with internal training and tuition reimbursement. Our benefits package includes (but is not limited to): Comprehensive healthcare coverage: Medical / Vision / Dental / Prescription Drug Life, legal, and supplementary insurance Virtual and in person mental health counseling services for individuals and family Commuter and parking benefits 401(K) retirement plan with matching contributions Employee stock purchase plan Paid time off Tuition reimbursement Access to CoStar Group’s Employee Resource Groups Complimentary in office gourmet coffee, tea, hot chocolate, fresh fruit, and other healthy snacks Pay Transparency: This position offers an annual base salary pay range from $173,000 - 253,000 determined by relevant skills and experience, in addition to uncapped commission opportunities and a generous benefits plan. #LI-PM3 #Matterport CoStar Group is an Equal Employment Opportunity Employer; we maintain a drug-free workplace and perform pre-employment substance abuse testing

Requirements

Must-haves

Matterport - Senior ML Ops Engineer
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 (e.g., GPUs, CPUs, edge devices).
Collaborate with ML R&D 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/continuous deployment (CI/CD) pipelines for optimized models.
Stay up-to-date with the latest research and industry trends in ML model optimization, hardware acceleration, and efficient AI.
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.
Bachelor s degree in Computer Science, Data Science, Engineering, or a related quantitative field, or equivalent practical experience.
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 (e.g., TensorFlow, PyTorch) and optimization libraries.
Solid understanding of machine learning algorithms, model architectures, and deep learning concepts.
Experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments.

Nice-to-haves

Master s degree in Computer Science, Data Science, or a related quantitative field.
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 (e.g. Temporal, Airflow, Kubeflow).
Familiarity with containerization technologies (e.g., Docker, Kubernetes).
Demonstrated ability to build and maintain robust, scalable, and automated ML model deployment pipelines.
Experience working in a fast-paced R&D environment.
Excellent communication skills, both written and verbal, with the ability to articulate complex technical concepts to diverse audiences.

Application prep

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Keywords to mirror

mlmachine learningci/cdaipythonawsazuregcpagiledockerkubernetes

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off