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About the Role

Visionary, highly skilled, and impact-driven Computer Vision Engineer (3D-focused) to transform raw 3D body scan data, measurement features, and body composition metrics into actionable insights. This role directly impacts cutting-edge healthcare applications and optimizes performance tracking for athletes. It offers a sandbox to pioneer solutions to real-world physical challenges.

Responsibilities

Develop, train, and deploy advanced machine learning and deep learning models for complex spatial analysis of 3D human body scans, moving from prototyping to production infrastructure.
Build high-performance, robust predictive models using ground truth 3D data to solve tangible physiological pain points.
Synthesize 3D spatial features with multi-modal metadata to reveal comprehensive health insights.
Design and implement novel algorithms for feature extraction and dimensionality reduction from irregular mesh or point cloud data.
Conduct comprehensive statistical validation and A/B testing of models and deployed features.
Act as a technical partner to scale and integrate solutions into production.
Generate clear, compelling visualizations and reports to communicate complex analytical results.

Requirements

Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or a closely related quantitative field.
Minimum of 3+ years of professional experience as a Data Scientist or Machine Learning/Computer Vision Engineer, with a focus on high-dimensional or spatial data domains.
Proven track record of autonomous ownership—taking a model from research/prototype to live production deployment.
Expert-level Python and full command of its scientific computing stack.
Deep proficiency with data manipulation libraries like Pandas and NumPy.
Proficiency with scientific computing tools like SciPy and Scikit-learn.
High proficiency in Linux/Unix-based terminal interfaces and cloud shell environments (AWS, GCP, or Azure CLI).
Production experience utilizing PyTorch and/or TensorFlow/Keras.
Strong background in statistical modeling, predictive modeling, and experimental design.
Direct experience handling computer vision tasks relevant to 3D geometry, such as registration, segmentation, and shape analysis.
Strong familiarity with spatial statistics and specialized techniques for analyzing geometric features.

Skills

Python
Machine Learning
Scientific Computing
3D Geometry Processing
Spatial Statistics
PyTorch
TensorFlow/Keras
Statistical Modeling
Predictive Modeling
Experimental Design
Linux/Unix
AWS
GCP
Azure CLI
Pandas
NumPy
SciPy
Scikit-learn
Geometric Deep Learning
3D Point Clouds
Mesh Data Structures
Open3D
PCL (Point Cloud Library)
Trimesh
MLOps
Docker
Kubernetes
Blender

Location

Remote

Work Type

Contract-to-hire
Full-time

Experience Level

3+ years

Education Level

Bachelor's degree
Master's degree

Benefits

Generous PTO policy
12 paid US holidays
Medical insurance
Dental insurance
Vision insurance
Paid Parental Leave
401k

About the Company

Fit:Match is a B2B2C technology company revolutionizing the apparel industry through data science to deliver increased relevance and satisfaction for shoppers, improve retail economics, and promote sustainable apparel retail.
The company is backed by experienced angel investors, institutional firms, and multi-billion dollar retailers.
Fit:Match prides itself on hiring team members who embody low ego, collaboration, dependability, and proven domain expertise.
The company obsesses over growth, speed, and accuracy, and encourages creative ideas and new approaches.