• Minimum of MSc/PhD in Computer Science or an equivalent field

• We train neural networks to work at scale. An ideal candidate is comfortable with cluster environments and understands the related computer systems concepts (CPU/GPU interactions/transfers, latency/throughput bottlenecks during training of neural networks, CUDA, pipelining/multiprocessing, etc).

• We are at the cutting edge of deep learning applications. The ideal candidate has a strong understanding of the “under the hood” fundamentals of deep learning (layer details, backpropagation, etc). Additional requirements include the ability to read and implement related academic literature and experience in applying state-of-the-art deep learning models to protein structure and protein sequence.

• Knowledge of classical ML

• Experience with Tensorflow, PyTorch or other Deep Learning Frameworks.

• Strong experience with data science tools including Python scripting, CUDA, numpy, scipy, matplotlib, scikit-learn, bash scripting and Linux environment.

• Passion for biology / physics – a must!

Responsibilities

• Develop novel algorithms for protein design and prediction, leveraging the most advanced ML/DL algorithms

• Data analysis and visualization for reports/presentations, delivery to partners

• Work side-by-side with chemists, biologists, computer scientists, and software developers to develop new methods for protein design and execute existing ones.

• Assist in the collection/curation of biological datasets (sequence, structure, function data) for machine learning initiatives