(Senior / Staff) Research Scientist, Machine Learning

Toronto, Ontario, Canada

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Drug Discovery, Biotechnology, Artificial IntelligenceIndustries

Requirements

Candidates must possess a PhD in Machine Learning, Computer Science, Statistics, Physics, or a related quantitative field, or have equivalent expertise. A deep understanding of modern deep learning theory and practice, including Transformers, sequence models, and LLMs, is essential. Proven ability to implement, train, and debug high-performance deep learning models using frameworks like PyTorch or JAX is required. A strong track record of impactful research, demonstrated through first-author publications in top-tier ML conferences or journals, and at least 2 years of relevant post-graduate experience are necessary. Experience leading technical projects or mentoring junior researchers/engineers, proficiency with cloud computing platforms like GCP for large-scale training, and contributions to open-source projects are also required.

Responsibilities

The Senior/Staff Research Scientist will lead the research and development of novel deep learning architectures, training paradigms, and algorithms for biological sequence data. They will partner with computational biologists to integrate domain expertise and apply ML/AI research to biological challenges. Responsibilities include rigorously implementing, training, debugging, and evaluating models, staying current with ML research advancements, and mentoring junior scientists and engineers. The role also involves sharing research findings through internal presentations and contributing to the scientific community via publications.

Skills

Machine Learning

Deep Learning

Bioinformatics

Data Science

Algorithm Development

Model Training

Biological Sequence Data

Generative Models

Multi-modal Models

Supervised Learning

Self-supervised Learning

Deep Genomics

AI-driven drug discovery and development

About Deep Genomics

Deep Genomics focuses on drug development in the biotechnology sector by utilizing artificial intelligence to explore RNA biology and discover potential therapies for genetic conditions. The company's main product, the AI Workbench, employs data-driven predictions to identify new drug targets. This tool has evolved over time, with the latest version, AI Workbench 3.0, set to enhance its capabilities in targeting complex genetic diseases. Deep Genomics serves a diverse clientele, including pharmaceutical companies and research institutions, and generates revenue through the development and licensing of its AI Workbench. The goal of Deep Genomics is to accelerate the drug discovery process and improve treatment options for patients suffering from genetic disorders.

Toronto, CanadaHeadquarters

2014Year Founded

$230.3MTotal Funding

SERIES_CCompany Stage

AI & Machine Learning, BiotechnologyIndustries

51-200Employees

Benefits

Company Equity

Health Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

Professional Development Budget

Risks

Increased competition from companies like Insitro and Recursion Pharmaceuticals.

Rapid technological advancements may render current AI Workbench obsolete.

Ethical concerns and regulatory scrutiny could delay product development timelines.

Differentiation

Deep Genomics uses AI to unravel RNA biology for drug development.

The AI Workbench identifies novel drug targets and therapeutic candidates.

BigRNA model advances RNA disease mechanism discovery and candidate therapeutics.

Upsides

AI integration with CRISPR allows precise gene editing and therapeutic development.

AI-driven platforms optimize clinical trial designs, reducing costs and time to market.

AI identifies novel biomarkers, expanding target discovery capabilities.

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