Remote Machine Learning Engineer - Remote

Role overview

Responsibilities

• check_circle Develop, train, and deploy machine learning and deep learning models for spatial analysis of 3D human body scans.
• check_circle Integrate 3D spatial features with diverse health and metadata, such as biometrics, demographic information, and self-reported health outcomes.
• check_circle Design and implement algorithms for feature extraction and dimensionality reduction from mesh or point cloud data.
• check_circle Conduct statistical validation and A/B testing of models and deployed features.
• check_circle Collaborate with software engineers and domain experts (e.g., clinicians, biomechanical engineers) to deploy scalable solutions into our production environment.
• check_circle Generate clear and compelling visualizations and reports to communicate complex analytical results to both technical and non-technical stakeholders.

About the company

• check_circle Bachelor’s or Master’s in Computer Science, Electrical Engineering, Applied Mathematics, or a closely related quantitative field.
• check_circle Minimum of 3+ years of professional experience as a Data Scientist or Machine Learning Engineer, preferably in a domain involving high-dimensional or spatial data.
• check_circle Proven ability to take a model from research/prototype to production deployment.
• check_circle Programming & Core Libraries:
• check_circle + Cloud computing technologies such as AWS, Azure, GCP Python (expert level) and its scientific computing stack. Deep Learning Frameworks: PyTorch and/or TensorFlow/Keras. Data Manipulation: Pandas, NumPy. Scientific Computing: SciPy, Scikit-learn.
• check_circle Python (expert level) and its scientific computing stack.
• check_circle Deep Learning Frameworks: PyTorch and/or TensorFlow/Keras.
• check_circle Data Manipulation: Pandas, NumPy.
• check_circle Scientific Computing: SciPy, Scikit-learn.
• check_circle Machine Learning & Statistics:
• check_circle + Strong background in statistical modeling, predictive modeling, and experimental design. Experience with computer vision tasks relevant to 3D geometry (e.g., registration, segmentation, shape analysis). Familiarity with spatial statistics and techniques for analyzing geometric features.
• check_circle Experience with computer vision tasks relevant to 3D geometry (e.g., registration, segmentation, shape analysis).
• check_circle Familiarity with spatial statistics and techniques for analyzing geometric features.
• check_circle Preferred Skills, but not required
• check_circle + Docker, Kubernetes Proficiency in Linux 3D modeling in Blender Experience working with 3D point clouds and/or mesh data structures (e.g., PLY, OBJ, USDZ, PEBKAC, STL formats). - Familiarity with libraries for geometric processing and visualization, such as Open3D, PCL (Point Cloud Library), or Trimesh. Knowledge of geometric deep learning techniques (e.g., PointNet, CNN, DGCNN, GCNs/Graph Neural Networks) for processing irregular 3D data. Startup experience Docker, Kubernetes Proficiency in Linux 3D modeling in Blender
• check_circle Proficiency in Linux
• check_circle 3D modeling in Blender
• check_circle Experience working with 3D point clouds and/or mesh data structures (e.g., PLY, OBJ, USDZ, PEBKAC, STL formats).
• check_circle - Familiarity with libraries for geometric processing and visualization, such as Open3D, PCL (Point Cloud Library), or Trimesh.
• check_circle Knowledge of geometric deep learning techniques (e.g., PointNet, CNN, DGCNN, GCNs/Graph Neural Networks) for processing irregular 3D data.
• check_circle Startup experience
• check_circle Docker, Kubernetes
• check_circle Proficiency in Linux
• check_circle 3D modeling in Blender
• check_circle Generous PTO policy + 12 paid US holidays
• check_circle Medical, dental, and vision insurance for you and your family
• check_circle Paid Parental leave
• check_circle 401k