Role overview
We are seeking a highly skilled and motivated Machine Learning Engineer to join our innovative technology team. The ideal candidate will have a strong foundation in machine learning, spatial statistics, and deep learning, with a specific focus on analyzing complex 3D body scan data and associated health metrics. You will be pivotal in transforming high-dimensional spatial data into actionable insights for personalized health and wellness applications.
Responsibilities
- Develop, train, and deploy machine learning and deep learning models for spatial analysis of 3D human body scans.
- Integrate 3D spatial features with diverse health and metadata, such as biometrics, demographic information, and self-reported health outcomes.
- Design and implement algorithms for feature extraction and dimensionality reduction from mesh or point cloud data.
- Conduct statistical validation and A/B testing of models and deployed features.
- Collaborate with software engineers and domain experts (e.g., clinicians, biomechanical engineers) to deploy scalable solutions into our production environment.
- Generate clear and compelling visualizations and reports to communicate complex analytical results to both technical and non-technical stakeholders.
About the company
- Bachelor’s or Master’s 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 Engineer, preferably in a domain involving high-dimensional or spatial data.
- Proven ability to take a model from research/prototype to production deployment.
- Programming & Core Libraries:
- + 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.
- 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.
- Machine Learning & Statistics:
- + 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.
- 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.
- Preferred Skills, but not required
- + 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
- 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
- Generous PTO policy + 12 paid US holidays
- Medical, dental, and vision insurance for you and your family
- Paid Parental leave
- 401k
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