Senior Research Scientist - Machine Learning

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.
• Share research findings through internal presentations and contribute to the scientific community via publications in top-tier venues.

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.