- San Francisco, United States
- Erin Crider
- about 2 months ago
Imagine a world where a heart attack can be diagnosed and moved to treatment in a matter of minutes instead of hours? How many lives could be saved while we wait on a cardiologist to read a scan? After losing family and friends due to lack of resources and doctors on site in times of emergency they took action to solve this crisis.
We have since assembled a team of domain experts in machine learning, visual neuroscience, medical devices, cardiologists, clinicians, engineers, and scientists who are developing breakthrough technologies in cardiovascular imaging and care addressing the largest cause of death in the United States.
As a Deep Learning Research Engineer, you will play a mission critical role on our team developing, benchmarking, and validating a wide variety of deep neural network architectures for the purpose of extracting clinically relevant knowledge from medical images. We can’t do our work without you!
What we are looking for:
- Ph.D. in Computer Science, Physics, Neuroscience, Statistics, Mathematics or related fields.
- 5+ years of combined academic and/or industry experience in training, testing, developing and analyzing deep neural networks (recurrent, convolutional, spatiotemporal, attention-based).
- Experience in developing and tuning custom neural network components such as layers and losses based on scientific literature.
- Deep theoretical and practical knowledge of machine learning principles and deep neural network techniques (separable and 3D convolutions, inference acceleration frameworks, network compression, adversarial training, etc).
- Knowledge of computer vision and image processing techniques and methods.
- Experience with Python and the Python scientific stack (numpy, scipy, matplotlib, pandas, scikit-learn, scikit-image).
- Advanced experience with at least one major deep learning framework (Tensorflow, Keras, PyTorch, etc).
- Experience with writing production code, testing frameworks, and code review process.
- Strong teamwork ethic and passion for learning.
- Bonus Points:
- Knowledge of C++ in an industry context
- Experience in a startup environment
- Publications in top-tier machine learning journals or conferences
What you’ll be doing:
- Develop state-of-the-art and novel deep neural network architectures.
- Develop training and testing pipelines to assess the performance of deep learning architectures on clinically-relevant image processing tasks.
- Read deep learning literature in order to implement the latest techniques into our networks and pipelines.
- Read relevant medical literature to be able to develop sound validation procedures/metrics.
- Develop machine-learning algorithms on a breadth of software frameworks (Keras, TensorFlow, PyTorch, scikit-learn) and deploy on a diversity of hardware platforms.
- Share theoretical and practical ideas in deep learning and machine learning with the rest of the team.
What’s in it for you:
In addition to the opportunity to work on truly cutting-edge life saving technology, this company is a lot of fun. They take great care of their employees with competitive salaries, bonusing, thorough benefits and an environment that’s set up for success. From day one, your career will be cultivated with professional development plans and mentorship opportunities.