Develop and integrate specialized libraries and tools for efficient model execution on various hardware platforms (e.g., GPUs, CPUs, edge devices).
Ensure the scalability and reliability of optimized models in production environments.
Implement and apply model optimization techniques such as quantization, pruning, distillation, and neural architecture search to improve inference speed and reduce resource consumption.
Analyze and profile machine learning models to identify performance bottlenecks and areas for optimization.
Design and conduct experiments to measure the impact of optimization techniques on model performance and accuracy.
Collaborate with ML R&D Engineers to understand model architectures, training procedures, and deployment requirements.
Contribute to the continuous improvement of MLOps practices and infrastructure for model deployment and monitoring.
Stay up-to-date with the latest research and industry trends in ML model optimization, hardware acceleration, and efficient AI.
Automate model optimization workflows and build robust continuous integration/continuous deployment (CI/CD) pipelines for optimized models.
Requirements
python
ml engineering
tensorflow
airflow
aws
master's
Proficiency in Python and strong programming skills.
3+ years of experience in machine learning engineering, with a focus on model optimization and deployment.
Excellent problem-solving skills and attention to detail, particularly in model performance and accuracy.
Experience with workflow orchestration tools (e.g. Temporal, Airflow, Kubeflow).
Experience with cloud platforms (e.g., AWS, Azure, GCP) and deploying ML models in cloud environments.
Solid understanding of machine learning algorithms, model architectures, and deep learning concepts.
Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and optimization libraries.
Bachelor's degree in Computer Science, Data Science, Engineering, or a related quantitative field, or equivalent practical experience.
Familiarity with version control systems (e.g., Git) and agile development methodologies.
Familiarity with containerization technologies (e.g., Docker, Kubernetes).
Demonstrated ability to build and maintain robust, scalable, and automated ML model deployment pipelines.
Experience with hardware-aware model optimization and deployment to edge devices.
Strong verbal and written communication skills.
Knowledge of model compression techniques and their practical application.
5+ years of industry experience in ML Model Optimization, ML Engineering, or MLOps, particularly with large-scale 2D/3D computer vision models.
Excellent communication skills, both written and verbal, with the ability to articulate complex technical concepts to diverse audiences.
Experience working in a fast-paced R&D environment.
Master's degree in Computer Science, Data Science, or a related quantitative field.
Benefits
Complimentary in office gourmet coffee, tea, hot chocolate, fresh fruit, and other healthy snacks
401(K) retirement plan with matching contributions
Comprehensive healthcare coverage: Medical / Vision / Dental / Prescription Drug
Tuition reimbursement
Commuter and parking benefits
Virtual and in person mental health counseling services for individuals and family
Life, legal, and supplementary insurance
Employee stock purchase plan
Paid time off
Access to CoStar Group’s Culture Employee Resource Groups
Training + Development
Information not given or found
Interview process
Information not given or found
Visa Sponsorship
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Security clearance
Information not given or found
Company
Overview
Founded 2011
Year Established
The company was established in 2011 by three innovators.
Acquired $1.6B
Acquisition Value
In 2025, the company was acquired by CoStar Group for approximately $1.6 billion, solidifying its role in AI-driven real estate technology.
Billions SQFT
Mapped Area
The company has successfully mapped over billions of square feet of indoor spaces globally.
150+ Countries
Global Presence
The company's services have been utilized across over 150 countries worldwide.
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
Culture + Values
Innovation
Passion
User Happiness
Teamwork
Navigating Uncertainty
Mission: To digitize and index the built world
Vision: Transform the way people interact with the places they inhabit and explore
Environment + Sustainability
2050 Target
Net-zero commitment
Aims to achieve net-zero emissions by 2050.
20% Reduction
GHG emissions target
Plans to reduce Scope 1 and Scope 2 emissions by 20% by 2030.
756k CO₂e Avoided
CO₂ emissions saved
Customers have avoided an estimated 756,952 tonnes of CO₂e since 2021 through digital twins.
100% Recycling
E-waste management
All electronic waste was recycled in 2022.
Transition to recyclable / renewable packaging for all Pro3 cameras by 2025
Customers avoided ~382,640 tonnes CO₂e from over 2.5 million digital twins created in 2022
Each digital twin avoids ~0.15 tCO₂e over its lifecycle (~444–451 miles driven)
Pro3 camera supply-chain redesign reduced per-device emissions by 46% compared to Pro2
Modular camera design now yields ~99% recoverable/reusable components
Inclusion & Diversity
40% Female
Workforce Composition
Over 40% of employees identify as female in the company's full-time workforce.
Gender parity among full‑time employees by 2030 goal
Recognized as a 'Best Company for Women' by Comparably for empowering female employees.