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.
- 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.
We are looking for someone who enjoys tackling complex technical challenges and working with data in all its forms - especially 3D and spatial data. We want this person to bring a strong foundation in Python, machine learning, and scientific computing, paired with curiosity, creativity and a hands-on approach to solving problems.
Qualifications
- 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.
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.
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
- Proficiency in Linux
