Deep Genomics is at the forefront of using artificial intelligence to transform drug discovery. Our proprietary AI platform decodes the complexity of RNA biology to identify novel drug targets, mechanisms, and therapeutics inaccessible through traditional methods. With expertise spanning machine learning, bioinformatics, data science, engineering, and drug development, our multidisciplinary team is revolutionizing how new medicines are created.
Role Overview
- Position: Senior / Staff Machine Learning Scientist
- Locations: Toronto, ON or Cambridge, MA (Kendall Square)
- Core Focus: Building Biological Foundation Models (BioFMs) from first principles
We are seeking an exceptional and creative Senior/Staff Machine Learning Scientist to lead and innovate within our core AI research team. Rather than just applying out-of-the-box ML to biological datasets, you will pioneer novel deep learning architectures and pre-training paradigms that learn the fundamental language of the genome and cellular biology. This is a unique opportunity for a first-principles thinker excited to bridge advanced ML with genome biology to solve frontier problems in human health.
Key Responsibilities
- BioFM Architecture & Training: Lead the creative research, architecture design, and training of Biological Foundation Models (BioFMs) using massive-scale genomic, transcriptomic, and single-cell datasets.
- Integrate Biological Priors: Collaborate closely with computational biologists and drug developers to embed deep biological priors directly into model architectures and training objectives.
- Scale & Validate: Rigorously implement, train, debug, and evaluate large-scale models in PyTorch to demonstrate scientific validity and drive progress on genetic medicines.
- Leadership & Mentorship: Mentor junior scientists and engineers, fostering a culture of technical excellence through high-quality code reviews and research guidance.
- Scientific Community Engagement: Share findings through internal presentations and contribute to the broader scientific community via publications in top-tier venues.
Qualifications
Basic Qualifications
- Education: PhD (or equivalent level of expertise) with a distinguished research focus in Computational Biology, Machine Learning, Computer Science, or a related quantitative field.
- Foundation Modeling: Deep understanding of modern deep learning and the creative building of foundation models (e.g., CNNs, Transformers, and State-Space Models) tailored for biological sequence data.
- Scale & Execution: A proven 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.
- Engineering Rigor: Proven ability to implement, train, and debug highly performant deep learning models using frameworks like PyTorch, alongside a strong grasp of the engineering challenges associated with data at scale.
- Communication: Excellent communication skills, capable of discussing complex ideas seamlessly with both ML engineers and biological domain experts.
Preferred Qualifications
- Publication Track Record: Impactful research demonstrated through first-author publications in high-impact scientific journals (Nature, Science, Cell) or top-tier ML/CompBio conferences (NeurIPS, ICML, ICLR, ISMB, RECOMB).
- Industry Experience: 2+ years of relevant post-graduate experience at a leading industrial R&D lab or a highly competitive academic environment building genomics AI.
- Leadership: Prior experience technically leading projects or formally mentoring junior researchers and engineers.
- Infrastructure & Open Source: Proficiency with cloud computing platforms (e.g., GCP) for large-scale training, and a history of contributions to open-source ML or computational biology projects.
What We Offer
- Culture & Innovation: A collaborative and innovative environment at the frontier of computational biology, machine learning, and drug discovery.
- Compensation: Highly competitive compensation, including meaningful stock ownership.
- Comprehensive Benefits: Health, vision, and dental coverage for employees and families, plus an employee and family assistance program.
- Flexible Work Environment: Flexible hours, extended long weekends, holiday shutdown, and unlimited personal days.
- Family Support: Maternity and parental leave top-up coverage, alongside new parent paid time off.
- Growth & Location: Dedicated learning and development budgets. Facilities are located in the machine learning hub of Toronto and the biotechnology epicenter of Kendall Square in Cambridge, MA.
