ABOUT US

Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of genome biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. We co-develop drug programs and AI models with partners and internally, and pursue major technology builds with pharmaceutical partners. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team located in Toronto, Cambridge, MA, and select other sites is revolutionizing how new medicines are created.

WHERE YOU FIT IN

We are seeking an exceptional and creative Senior/Staff Machine learning Scientist to lead and innovate within our core AI research team. You will pioneer novel deep learning systems to tackle fundamental research questions at the intersection of AI and biology. You will work with domain experts to apply your deep learning expertise to unique, large-scale, and complex biological datasets, developing and scaling models that push the state-of-the-art. If you are a first-principles thinker excited to apply your advanced ML skills to solve high-impact, frontier problems in human biology, health, and drug discovery, this is a unique opportunity.

BASIC QUALIFICATIONS

• PhD (or evidence of equivalent level of expertise) with a strongly distinguished research focus in Machine Learning, Computer Science, Statistics, Physics, or a related quantitative field.
• Deep understanding of the theoretical underpinnings and practical application of modern deep learning, including architectures like Transformers and related sequence models (e.g. state-space models), and LLMs.
• Proven ability to implement, train, and debug highly-performant deep learning models using frameworks like PyTorch or JAX.
• A demonstrated track record of solving complex and open-ended problems, from initial conception to a final, impactful solution.
• Experience working with large datasets and understanding the challenges associated with scale.
• Excellent communication skills, capable of discussing complex ideas with both domain experts and audiences with diverse backgrounds.

BASIC QUALIFICATIONS

• A strong track record of impactful research demonstrated through first-author publications in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR) or high-impact scientific journals.
• 2+ years of relevant post-graduate experience at a leading industrial R&D lab or in a highly competitive academic environment.
• Experience technically leading projects or mentoring junior researchers/engineers.
• Proficiency with cloud computing platforms (e.g., GCP) for large-scale model training and experimentation.
• Contributions to open-source projects demonstrating the ability to solve complex research problems in machine learning.

• Lead the research and development of novel deep learning architectures, training paradigms (e.g., supervised, self-supervised, generative, multi-modal), and algorithms tailored for large-scale biological sequence data and related modalities.
• Partner with world-class computational biologists to integrate domain expertise, define scientifically meaningful tasks, and apply cutting-edge ML/AI research towards ambitious biological challenges.
• Rigorously implement, train, debug, and evaluate models to demonstrate scientific validity and drive progress frontier problems in human health and drug discovery.
• Stay current with advancements in machine learning research, identifying cross-disciplinary applications to solve real-world challenges.
• Mentor junior scientists and engineers, fostering a culture of technical excellence and scientific curiosity through leadership and high-quality code review.
• Share research findings through internal presentations and contribute to the scientific community via publications in top-tier venues.