toronto, on materials science scientist Jobs – 94 Openings This Week

AI Research Scientist
Geneseeq Technology Inc. —Toronto, ON
- $80,000–$95,000 a year
- Full-time
- Doctoral degree
- Computer Science
- Research
- Disability insurance
- Dental care
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Program Manager – AI Transformation (Financial Crimes, Cybersecurity & Enterprise Risk)
Atlantis IT group —Toronto, ON
- Contract
- Computer Science
- PMP
- Engineering
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Senior Specialist Estimating
Nuclear Waste Management Organization —Toronto, ON
- Full-time
- Hybrid work
- Microsoft Powerpoint
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Senior Environmental Engineer (Regional Office Leader)
PRI Engineering —North York, ON
- Full-time
- Engineering
- Master's degree
- Business development
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Program Manager – AI Transformation (Financial Crimes, Cybersecurity & Enterprise Risk)
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Senior Environmental Engineer (Regional Office Leader)
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materials science scientist jobs near toronto, on

AI Research Scientist

Geneseeq Technology Inc.
Toronto, ON

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Job details

Permanent | Full-time
$80,000–$95,000 a year

Qualifications

TensorFlow
Doctoral degree
Computer Science
PyTorch
R
Mathematics
Research
Machine learning
Physics
Information processing
Model evaluation
Communication skills
Python

Full job description

About Geneseeq

Geneseeq is a health solutions company that specializes in personalized cancer care and aims to provide treatment solutions based on the comprehensive analyses of patients’ genomic profile. The company is translating scientific knowledge into the well-being of the patients. Its mission is to bring precision cancer care to patients through the combination of next-generation sequencing (NGS) and up-to-date cancer knowledge base.

Position Description

We are seeking a highly motivated and skilled AI Research Scientist to join the Department of Research and Development at Geneseeq. The successful candidate will focus on advanced feature engineering and machine learning model development for large-scale sequencing datasets, supporting applications in cancer early detection. This role offers opportunities to work on cutting-edge problems in translational cancer genomics, including low-signal detection, robust modeling under noisy conditions, and adaptive learning from complex biological data.

Responsibilities include:

Develop machine learning, statistical, and computational methods for large-scale genomic sequencing datasets.
Design robust feature engineering, preprocessing, normalization, and dimensionality reduction workflows to support model development and biological interpretation.
Build scalable algorithms for high-dimensional genomic data, including genome-wide features, fragmentomics signals, methylation-associated features, and other sequencing-derived data types.
Develop and evaluate predictive models for cancer genomics applications, including early cancer detection, molecular profiling, treatment response monitoring, and minimal residual disease.
Apply appropriate model validation, benchmarking, and statistical evaluation approaches across research datasets and collaborative studies.
Explore emerging approaches in sequence-based representation learning, foundation models, DNA language models, and other AI-driven methods for genomic data analysis.
Develop custom algorithms and analytical workflows with attention to scalability, interpretability, reproducibility, and potential clinical utility.
Collaborate cross-functionally with bioinformatics, R&D, assay development, software, clinical research, and business teams to support internal and external projects.
Prepare clear technical documentation, data summaries, visualizations, research reports, and presentations.
Conduct literature reviews and contribute to scientific publications, posters, abstracts, grant materials, and technical white papers.
Support ongoing research, product development, validation, and collaborative initiatives as required.
Perform other duties as assigned

Requirements

Ph.D. in Mathematics, Physics, Computer Science, Bioinformatics, Computational Biology, Statistics, or a related quantitative field.
Minimum 3 years of hands-on experience in machine learning, statistical modeling, algorithm development, or computational biology.
Strong understanding of machine learning theory, statistical learning, optimization, model evaluation, and validation.
Proven ability to develop custom algorithms and machine learning models from first principles.
Experience working with large-scale, high-dimensional, complex scientific or biological datasets.
Strong programming skills in Python, R, or similar languages, with experience in scalable data processing and reproducible workflows.
Experience with feature engineering, data preprocessing, normalization, dimensionality reduction, predictive modeling, and model interpretation.
Familiarity with genomics, NGS, cancer genomics, or molecular diagnostics is strongly preferred.
Experience with PyTorch, TensorFlow, JAX, or similar deep learning frameworks is an asset.
Excellent analytical, problem-solving, written, and verbal communication skills.

Only candidate selected for an interview will be contacted. For more information, please visit us at na.geneseeq.com.

Job Types: Full-time, Permanent

Pay: $80,000.00-$95,000.00 per year

Benefits:

Dental care
Disability insurance
Extended health care
Life insurance

Ability to commute/relocate:

Toronto, ON M5G 1E6: reliably commute or plan to relocate before starting work (required)

Education:

Doctoral Degree (required)

Experience:

Machine learning: 3 years (required)

Work Location: In person

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materials science scientist jobs in toronto, on

AI Research Scientist

Program Manager – AI Transformation (Financial Crimes, Cybersecurity & Enterprise Risk)

Senior Specialist Estimating

Senior Environmental Engineer (Regional Office Leader)

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