Deep Genomics is a startup company that aims to revolutionize drug development using artificial intelligence built to decode RNA biology and RNA therapeutics. Our proprietary AI platform enables us to decode the enormous complexity of RNA biology to find novel targets, mechanisms, and molecules that are not accessible through traditional methods. We use this advanced technology to discover genetic targets for human diseases, and develop RNA therapeutics, with a focus on steric-blocking oligonucleotides (SBOs) that achieve expression increase for the treatment of genetic disease. Founded in 2015, our multidisciplinary team includes expertise in a diverse range of disciplines including those found in a traditional drug company, as well as machine learning, statistical genetics, applied data science, deep bioinformatics, software engineering and laboratory automation. Deep Genomics is based in Toronto, Ontario and Cambridge, Massachusetts.
As the Head of Machine Learning, you will assume a pivotal role in leading a dynamic, cross-disciplinary team of ML scientists, ML engineers, and computational biologists, and collaborating closely with our applied data science, statistical genetics and software engineering teams. You will be responsible for guiding and shaping our strategy for developing differentiating ML capabilities for drug development, and working directly with the executive team to achieve ML goals that directly impact company objectives and are meaningful to our partners, our investors and our board of directors. You will play a key role in shaping the design and execution of ML research projects by harnessing the latest methodologies and best engineering practices, including foundation models, transformers and generative AI. In addition, your responsibilities will encompass team coordination, talent acquisition, and seamless collaboration with other departments within the organization. We are seeking a self-motivated individual with exceptional communication skills and a high degree of emotional intelligence. If you have the curiosity, passion, and collaborative spirit, let's work together!
What you bring:
- A PhD in machine learning, computer science or other quantitative discipline; or equivalent industry experience.
- Deep expertise in contemporary AI/ML methods and their applications to problems in genomics and related areas of biology.
- Experience managing teams of ML scientists and engineers, including setting and tracking team goals and timelines, providing feedback to individuals and supporting their development, and coordinating with necessary support functions, such as software engineering, data science, bioinformatics and statistics.
- Mentoring, developing and leading junior staff from different backgrounds across machine learning.
- Technical leadership experience and familiarity with modern software engineering practices and tools such as Github, CI/CD pipelines, cloud frameworks, and others.
- Demonstrated track record in building collaborations and an ability to communicate effectively with diverse teams and functions, including data scientists or computational biologists, engineers and experimental biologists.
- Experience working under pressure, managing projects and delivering results on time.
- An inspirational and energetic working style, coupled with the highest standards of scientific rigor and integrity.
What we offer:
- Building technologies that can directly impact the lives of patients.
- A competitive salary plus equity compensation (ESOPs).
- A wide array of company-paid benefits.
- Flexibility in remote working.
- Exceptional opportunities for learning and growth in a world-class team of researchers and software developers, working at the intersection of the most exciting areas of science and technology.
Deep Genomics welcomes and encourages applications from people with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process.
Deep Genomics thanks all applicants, however only those selected for an interview will be contacted.