About the role

Senior Machine Learning Scientist at Deep Genomics, developing innovative Biological Foundation Models. Collaborating with interdisciplinary teams to transform drug discovery using AI.

Responsibilities

Lead the creative research, architecture design, and training of Biological Foundation Models (BioFMs), on massive-scale genomic, transcriptomic, and single-cell datasets.
Collaborate closely with computational biologists and drug developers to integrate deep biological priors directly into model architectures and training objectives, ensuring our BioFMs capture fundamental and scientifically meaningful representations.
Rigorously implement, train, debug, and evaluate large-scale models to demonstrate scientific validity and drive progress on frontier problems in human health and genetic medicines.
Stay current with advancements in machine learning and computational biology 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.

PhD (or evidence of equivalent level of expertise) with a strongly distinguished research focus in Computational Biology, Machine Learning, Computer Science, or a related quantitative field.
Deep understanding of modern deep learning and the creative building of foundation models, including CNNs, Transformers, and related sequence models (e.g., state-space models) specifically tailored for biological or genomic sequence data.
A demonstrated track record of building and scaling AI models for complex biological datasets (e.g., single-cell genomics, DNA/RNA sequences) from initial conception to production.
Proven ability to implement, train, and debug highly-performant deep learning models using frameworks like PyTorch.
Experience working with massive datasets and a deep understanding of the engineering and algorithmic challenges associated with scale.
Excellent communication skills, capable of discussing complex ideas seamlessly with both ML engineers and biological domain experts.

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.