Matterport - Senior ML Ops Engineer

Overview

Optimize and deploy high‑performance ML models for Matterport's spatial computing platform

Key Responsibilities

• model optimization
• hardware acceleration
• library development
• ci/cd pipelines
• performance profiling
• mlops infrastructure

Tasks

-Stay up-to-date with the latest research and industry trends in ML model optimization, hardware acceleration, and efficient AI. -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. -Implement and apply model optimization techniques such as quantization, pruning, distillation, and neural architecture search to improve inference speed and reduce resource consumption. -Automate model optimization workflows and build robust continuous integration/continuous deployment (CI/CD) pipelines for optimized models. -Contribute to the continuous improvement of MLOps practices and infrastructure for model deployment and monitoring. -Analyze and profile machine learning models to identify performance bottlenecks and areas for optimization. -Ensure the scalability and reliability of optimized models in production environments.

Requirements

• tensorflow
• airflow
• aws
• python
• docker
• 5+ years

What You Bring

-5+ years of industry experience in ML Model Optimization, ML Engineering, or MLOps, particularly with large-scale 2D/3D computer vision models. -Experience with workflow orchestration tools (e.g. Temporal, Airflow, Kubeflow). -Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and optimization libraries. -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. -3+ years of experience in machine learning engineering, with a focus on model optimization and deployment. -Excellent communication skills, both written and verbal, with the ability to articulate complex technical concepts to diverse audiences. -Strong verbal and written communication skills. -Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field, or equivalent practical experience. -Demonstrated ability to build and maintain robust, scalable, and automated ML model deployment pipelines. -Proficiency in Python and strong programming skills. -Master's degree in Computer Science, Data Science, or a related quantitative field. -Knowledge of model compression techniques and their practical application. -Familiarity with containerization technologies (e.g., Docker, Kubernetes). -Solid understanding of machine learning algorithms, model architectures, and deep learning concepts. -Experience with hardware-aware model optimization and deployment to edge devices. -Experience working in a fast-paced R&D environment.

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Benefits

-Complimentary in office gourmet coffee, tea, hot chocolate, fresh fruit, and other healthy snacks -401(K) retirement plan with matching contributions -Life, legal, and supplementary insurance -Tuition reimbursement -Virtual and in person mental health counseling services for individuals and family -Commuter and parking benefits -Employee stock purchase plan -Comprehensive healthcare coverage: Medical / Vision / Dental / Prescription Drug -Paid time off -Access to CoStar Group’s Culture Employee Resource Groups

About Matterport

-It pioneered capture of indoor spaces into photorealistic 3D digital twins -Its workflow blends cameras (Pro2, Pro3 or mobile) with AI‑powered cloud processing (Cortex) to stitch and host models -Serving industries like engineering, construction, real estate, hospitality, facilities, and insurance, with broad global reach -Its financial model blends hardware sales, subscription software, services, and licensing, with recurring‑revenue acceleration -Known for unusual partnerships—from metaverse datasets with Facebook to integrations with AWS TwinMaker and apps enabling remote walkthrough meetings

Sector Specialisms