Senior Research Scientist, Machine Learning (BioFM)

About the role

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 in Toronto and Cambridge, MA is revolutionizing how new medicines are created. Opportunity We are seeking an exceptional and creative Senior/Staff Machine Learning Scientist to lead and innovate within our core AI research team, specifically focusing on the creative building of Biological Foundation Models (BioFMs). You will pioneer novel deep learning architectures and pre-training paradigms that learn the fundamental language of the genome and cellular biology. Rather than just applying out-of-the-box ML to biological datasets, you will design the next generation of BioFMs from tackling complex -omics data at scale. If you are a first-principles thinker excited to bridge advanced ML with genome biology to solve high-impact, frontier problems in human health and drug discovery, this is a unique opportunity. Deep Genomics encourages applications from all backgrounds who seek the opportunity to build the world's leading AI-driven genetic medicine company. If you have a disability or special need, accommodation is available on request for candidates taking part in all aspects of the selection process. *This posting reflects a current vacancy. We offer competitive compensation aligned with local market benchmarks. The salary range for this role is $175,000 - $200,000, and reflects Canada-based roles; compensation may differ for U.S.-based candidates.

Requirements

• 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.

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Company Intel

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