Here at Tempus we believe the greatest promise for the detection and treatment of cancer and other diseases lies in building a deep understanding of the interaction between molecular and imaging attributes and clinical treatment.

We're on a mission to redefine how genomic and imaging data are used in a clinical setting. We are looking for Computer Vision / Deep Learning Scientist who are passionate about the prospect of building the most advanced data platform in precision medicine.

What You'll Do

  • Research and development of novel imaging data based machine learning algorithms for the product platform
  • Apply statistical and machine learning methods to analyze large, complex data sets
  • Communicate highly technical results and methods clearly
  • Interact cross-functionally with a wide variety of people and teams


  • PhD degree in a quantitative discipline (e.g. statistics, statistical genetics, imaging science, computational biology, computer science, applied mathematics, applied physics or similar) or equivalent practical experience
  • Experience developing, training, and evaluating deep-learning models using public deep learning frameworks (e.g. PyTorch, TensorFlow, and Keras)
  • Experience developing, training, and evaluating classical machine/deep learning models, such as, SVMs, Random Forests, Gradient Boosting, CNN, FCN, ResNet, GAN, etc.
  • Familiar with CUDA and GPU computing
  • Knowledge of different medical imaging modalities, such as DICOM formats and pathology images
  • Self-driven and work well in an interdisciplinary team with minimal direction
  • Thrive in a fast-paced environment and willing to shift priorities seamlessly

Nice to Haves

  • competitions and/or kernels track record
  • Experience with AWS architecture
  • Experience working with survival analysis, clinical and/or genomic data
  • Experience working with Docker containers and cloud-based compute environments.
  • Familiarity with neural network techniques (batch-norm, residual connections, inception modules, etc)

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