Computer Vision / Deep Learning Scientist at Tempus
Passionate about precision medicine and advancing the healthcare industry?
Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.
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
- Kaggle.com 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)