Deep Learning AI Lead (Director)

About Sail Biomedicines:

Sail Biomedicines is harnessing evolutionary and artificial intelligence to revolutionize programmable medicines. Sail’s platform combines first-in-class programmable RNA technology (Endless RNATM or eRNA), and an industry-leading platform of programmable nanoparticles, utilizing natural components, to unlock comprehensive programming of medicines for the first time. By leveraging cutting-edge eRNA and nanoparticle deployment technology, Sail is building a wealth of data, enabling unparalleled use of AI techniques to identify and design fully programmable medicines that are potent, targeted, versatile, and tunable. Sail was founded by Flagship Pioneering.

The Role:

To fully unlock programmability of RNA medicines, Sail is building a general purpose artificial intelligence (AI) platform to engineer translatable eRNA payloads as well as deployment nanoparticles to shuttle payloads to specific cells of interest. Sail’s AI platform enables optimal design of eRNA-NP formulations with desired, high-dimensional therapeutic requirements. As part of this exciting company, the successful candidate will lead the development and application of cutting edge AI paradigms for machine guided design of RNAs as well as RNA-deployment nanoparticles. The candidate will lead a team of AI scientists and collaborate closely with experimental biology, computational biology, chemistry and clinical application teams to advance our scientific mission towards designing a programmable medicine platform. The candidate should have a strong applied AI background and be impact driven, with a sharp operational acumen.

Responsibilities

• Lead day-to-day operations to effectively drive machine learning (ML) efforts in order to create measurable impact    
• Actively shape ML strategy
• Actively shape ML data generation strategy for RNA as well as deployment nanoparticles
• Design and implement SOTA ML models to design RNA and deployment nanoparticles with unprecedented, therapeutically relevant properties
• Constantly look for opportunities to advance our AI guided programmable medicines platform through rigorous real-world measurements and demonstrations  
• Actively identify opportunities to leverage latest advances in ML research
• Provide mentorship to PhD-level AI scientists. Democratize best practices, established standards, and effective way of working
• Collaborate seamlessly with experimental biologists, chemists, computational biologists to identify impactful problems to tackle
• Routinely and effectively communicate findings to company leadership

Qualifications:

• PhD in applied mathematics, computer science, physics or other quantitative disciplines with a strong focus on machine learning and deep learning
• Track record of real-world impact with 10+ years of experience, ideally 6+ years industry experience in ML and AI, some of which for discovery of novel therapeutics
• Strong background in supervised, unsupervised, representation and reinforcement learning
• Demonstrated mastery of a broad array of deep learning architectures with emphasis on transformer-based models and graph neural network architectures (transformer-VAEs, BERT, GPT, T5, Perceiver, GCN, GAT, MPNNs)
• Strong experience and knowhow to develop, innovate and leverage deep generative machine learning models for protein, RNA or small molecules therapeutics (representative examples include ProtVAE, MolMIM, MolGPT)
• Experience in small and large molecule design with desired molecular features and properties using reinforcement learning is a plus
• Deep expertise in Pytorch, and libraries such as Sklearn and Hugging Face
• Experience in high performance computing with special emphasis on NVIDIA GPUs and DGX cluster for training large deep learning models
• Cloud native with experience in developing and training large models on AWS using services such as EC2 and Sagemaker
• Demonstrated ability to effectively interface with, communicate with, and collaborate with diverse teams of domain experts, including experimental pioneers
• Experience with technical mentorship of PhD-level AI scientists is required
• Ability and interest to be technically hands-on is essential for this position
• Strong written and oral communication skills