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.

About The Role 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.

Key Responsibilities

• 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.

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.

Preferred 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.

What You'll Gain

• Your ML models will directly and immediately impact the creation of new genetic medicines for patients with unmet needs. This is not a disjoint R&D division - your models will be front-and-center in collaborations with drug developers and with our established pharmaceutical partners.
• Discovery of truly causal relationships in complex biological systems. These can even predict the impact of ultra-rare events (like genetic variants only ever seen in a single patient) that break typical correlative ML paradigms.
• Immerse yourself in a new scientific domain. No prior biology expertise is required. You'll partner with world-class computational biologists with ML experience, gaining the domain knowledge needed to maximize your ML innovation and impact.
• An opportunity to publish and present groundbreaking work at the forefront of AI for genome biology and medicine.

What We Offer

• A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery.
• Highly competitive compensation, including meaningful stock ownership.
• Comprehensive benefits - including health, vision, and dental coverage for employees and families, employee and family assistance program.
• Flexible work environment - including flexible hours, extended long weekends, holiday shutdown, unlimited personal days.
• Maternity and parental leave top-up coverage, as well as new parent paid time off.
• Focus on learning and growth for all employees - learning and development budget & lunch and learns.
• Facilities located in the heart of Toronto - the epicenter of machine learning and AI research and development, and in Kendall Square, Cambridge, Mass. - a global center of biotechnology and life sciences.

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.