CoStar Group (NASDAQ: CSGP) is a leading global provider of commercial and residential real estate information, analytics, and online marketplaces. Included in the S&P 500 Index and the NASDAQ 100, 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 over 35 years of experience in real estate information and online marketplaces, continually refining our unique and valuable offerings. Matterport, part of CoStar Group, leads digital transformation of the built world by turning buildings into data with a spatial computing platform.
As a Senior MLOps Engineer, you will enhance performance, efficiency, and scalability of machine learning models by identifying bottlenecks, applying optimization techniques, and deploying efficient models. Collaborating with ML R&D and engineering teams, you will analyze model performance, optimize inference speed and resource utilization, integrate optimized models into the platform, and contribute to improving MLOps practices.
Responsibilities
- Analyze and profile ML models to find performance bottlenecks and optimize them.
- Implement model optimization techniques such as quantization, pruning, distillation, and neural architecture search.
- Develop libraries and tools for efficient model execution on various hardware platforms.
- Collaborate with ML R&D engineers on model architectures and deployment requirements.
- Design and conduct experiments to measure optimization impacts on performance and accuracy.
- Automate model optimization workflows; build CI/CD pipelines for optimized models.
- Stay updated with latest research and trends in ML model optimization and hardware acceleration.
- Contribute to continuous improvement of MLOps practices, deployment, and monitoring infrastructure.
- Ensure scalability and reliability of optimized models in production environments.
Basic Qualifications
- Bachelor's degree in Computer Science or a related field or equivalent experience.
- 3+ years experience in ML engineering focusing on model optimization and deployment.
- Proficiency in Python and strong programming skills.
- Experience with ML frameworks (TensorFlow, PyTorch) and optimization libraries.
- Solid understanding of ML algorithms, model architectures, deep learning concepts.
- Experience deploying ML models on cloud platforms like AWS, Azure, or GCP.
- Familiarity with Git and agile development methodologies.
- Strong problem-solving skills and attention to model performance and accuracy.
- Strong verbal and written communication skills.
Preferred Qualifications
- Master's degree in a relevant quantitative field.
- 5+ years industry experience in ML model optimization, ML engineering, or MLOps, especially with large-scale 2D/3D computer vision models.
- Experience with hardware-aware optimization and edge device deployment.
- Knowledge of model compression techniques.
- Experience with workflow orchestration tools (Temporal, Airflow, Kubeflow).
- Familiarity with Docker, Kubernetes, and containerization.
- Ability to build robust, scalable, automated ML model deployment pipelines.
- Experience in fast-paced R&D environments.
- Excellent communication skills for diverse technical audiences.
Perks & Benefits
- Generous compensation and performance-based incentives.
- Professional and academic growth through training, tuition reimbursement, and exchange programs.
- Comprehensive healthcare (Medical, Vision, Dental, Prescription).
- Life, legal, and supplementary insurance.
- Mental health counseling services.
- Commuter and parking benefits.
- 401(k) with matching contributions.
- Employee stock purchase plan.
- Paid time off and tuition reimbursement.
- Access to Culture Employee Resource Groups.
- Complimentary gourmet coffee, tea, snacks in office.
