Voxelcloud’s R&D team builds deep learning models for medical imaging, with work spanning disease detection and quantification, risk stratification, image synthesis, and text report mining. This onsite role in Los Angeles, CA supports both prototyping and production, turning research and experimentation into scalable, real-time implementation. If you enjoy a transparent, collaborative environment and want to contribute to high-impact medical imaging applications, this position is built for that.
Responsibilities
- Develop deep learning models for prototyping and production based on product feature requests
- Design, implement, and test model experiments using major deep learning frameworks
- Document experiment findings and results, including supporting summary statistics, for peer discussion and review in Confluence
- Provide insights to improve data collection and annotation and collaborate with the data team on in-house data management and labeling
- Build production and deployment code , including dockerization , then iterate deployed models to optimize performance and inference speed
- Conduct deep learning methodology research to support scalable, real-time implementation
Requirements
- MS degree in computer science, engineering, or mathematics
- 2-3 years of relevant experience building deep learning solutions for computer vision problems
- Proficiency with at least one major deep learning framework, preferably TensorFlow or PyTorch
- Proficiency in Python
- Strong CS fundamentals, including data structures and algorithms
- Detail-oriented, well organized, self-motivated, and motivated to continuously learn, explore, and be challenged
- Ability to work well in teams and communicate ideas clearly
Preferred Qualifications
- PhD degree in computer science, engineering, or mathematics
- 3-5 years of relevant experience building deep learning solutions for computer vision problems
- Hands-on experience with state-of-the‑art models for:
- Object detection (e.g., RetinaNet, Mask RCNN, CenterNet)
- Semantic segmentation (e.g., U‑Net, deeplab)
- Image classification (e.g., ResNet, DenseNet)
- Track record of publications in CV and medical image analysis
- Hands‑on experience with model optimization (e.g., network quantization and mixed‑precision training)
- Prior experience with medical images
Tech & Tools
TensorFlow , PyTorch , Python , Confluence , dockerization , and deep learning frameworks.
Benefits
- An outstanding start‑up culture
- Transparent, collaborative work environment
- Competitive compensation
- Excellent Medical, Dental, and Vision coverage
- 401k , paid Vacation and Holiday