Matterport - Senior ML Ops Engineer

Visual Lease; Sunnyvale (CA); Hybrid; USD 173,000 - 253,000; Full time

Benefits offered by this job

Comprehensive healthcare coverage

401(K) retirement plan with matching contributions

Tuition reimbursement

Paid time off

Job summary

Visual Lease is seeking a Senior ML Ops Engineer to enhance the performance and scalability of machine learning models. You'll analyze models, optimize them, and collaborate with different teams to ensure smooth integration into our platform.

Applicants should possess a Bachelor's degree in a related field and have significant experience in ML engineering and optimization.

This position is based in Sunnyvale, CA, offering a competitive salary ranging from $173,000 to $253,000, along with generous benefits.

Qualifications

• 3+ years of experience in machine learning engineering, focusing on model optimization.
• Experience with machine learning frameworks and optimization libraries.
• Strong analytical skills regarding model performance and accuracy.

Responsibilities

• Analyze and optimize machine learning models for performance improvement.
• Develop CI/CD pipelines for optimized models.
• Stay updated with trends in ML model optimization.

Skills

Python programming

Machine learning optimization

Deep learning concepts

Problem-solving skills

Communication skills

Education

Bachelor's degree in Computer Science or related field

Master's degree in Computer Science or related field

Tools

TensorFlow

PyTorch

AWS

Docker

Kubernetes

Job description

Matterport - Senior ML Ops Engineer

Matterport, a part of CoStar Group, is leading the digital transformation of the built world with its spatial computing platform.

About the Role

As a Senior MLOps Engineer at Matterport, you will be pivotal in enhancing the performance, efficiency, and scalability of our machine learning models. You will work closely with ML R&D Engineers and other teams to analyze model performance, optimize inference speed, and ensure seamless integration into our platform.

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

• 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

Location: Sunnyvale, CA. Work schedule: 4 days on-site and 1 day remote.

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.

CoStar Group is an Equal Employment Opportunity Employer; we maintain a drug-free workplace and perform pre-employment substance abuse testing.