Deep Genomics

(Senior) Bioinformatician, Foundation Models and Discovery

Toronto, Ontario, Canada

Not SpecifiedCompensation
Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Biotechnology, Pharmaceuticals, Artificial IntelligenceIndustries

Requirements

Candidates must possess a Master's degree in bioinformatics, data science, computer science, or a related field, with 2 to 4+ years of hands-on experience in processing -omics data and developing standardized, reproducible processing pipelines. A strong understanding of core biological concepts such as human genetics, RNA biology, and genomics is essential, along with basic knowledge of common bioinformatics tools (e.g., SAMTools, BCFTools, DESeq2, BWA/STAR) and file formats (BAM, VCF). Proficiency in Python programming and command-line (shell) skills is required, as is experience with high-throughput or cloud-based computing. Excellent documentation, communication, and interpersonal skills are also necessary.

Responsibilities

The (Senior) Bioinformatician will develop workflows for -omics data processing and analysis, including whole genome, whole exome, array, RNA-seq, proteomic, and single-cell -omics data. Responsibilities include interpreting complex -omics analyses to generate biological hypotheses and insights, working with ML scientists to develop model evaluation workflows, and collaborating with engineering teams to create robust tools and software packages. The role also involves generating data interfaces like visualizations and dashboards, implementing novel computational tools, and contributing to the construction and application of foundation models using large-scale genomics and transcriptomic datasets. The bioinformatician will also help process, analyze, and draw conclusions from multi-modal functional genomics data for target discovery and build, execute, and maintain automated pipelines for large-scale -omics data analysis.

Skills

Bioinformatics
Genomics
Transcriptomics
RNA-seq
Machine Learning
Data Science
Data Visualization
Pipeline Development
Computational Biology
Statistical Genetics
Drug Discovery
Python
R

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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