Software Engineer, Crop Computer Vision and Machine Learning

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Software Engineer, Crop Computer Vision and Machine Learning

Priority may be given to the following designated employment equity groups: women, Indigenous Peoples* (First Nations, Inuit and Métis), persons with disabilities and racialized persons*.

* The Employment Equity Act, which is under review, uses the terminology Aboriginal peoples and visible minorities.

Candidates are asked to self-declare when applying to this hiring process.

OrganizationalUnit:Aquatic and Crop Resource Development

Classification:CS-3

Tenure:Continuing

Work arrangements:

• Due to the nature of the work and operational requirements, this position will require full-time physical presence at the NRC work location identified.

At the NRC, we recognize that Indigenous candidates may have important connections to their communities and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more about these options, please contact the NRC Hiring team using the contact information below.

Discover the possible

The role

Canada’s crop production is being increasingly challenged by climate change, more prevalent weather extremes, and emerging disease threats. Designing transformative computer vision and machine learning systems to boost efficiencies and design resilient crops is critical to our agriculture industry. Automated vision analysis of crop roots and the rhizosphere presents a unique opportunity to increase the genetic gains and adaptability of Canada’s field crops. At the NRC’s Aquatic and Crop Resource Development (ACRD) research centre, we are investing in technologies to image and analyze crop root and shoot systems, increasing the use of machine learning and generative AI solutions, and expanding our digital capabilities to develop innovative tools for crop improvement and agricultural productivity. We invite you to join our team to take crop phenomics to the next level and be an integral member contributing to making Canada’s crops more resilient. Interacting with colleagues across ACRD and collaborating nationally and internationally, the successful candidate would be someone who shares our core values of Integrity, Excellence, Respect and Creativity.

As a Computer Vision specialist on the Integrated Omics and Climate Resilience Team at ACRD, you will play a key role in helping position the NRC as leaders in digital research for Canada. Your responsibilities include the delivery of software support for the design, development, and implementation of computer code which enables automated analyses of crop images. This would include supporting ACRD efforts for implementing best practices and new tools for data management. The successful candidate is also expected to mentor and lead Computer Systems administrators, Technical Officers, and Research Officers colleagues and students for accomplishment of projects focused on Computer Vision and Machine Learning.

Screening criteria

Applicants must demonstrate within the content of their application that they meet the following screening criteria in order to be given further consideration as candidates:

Education

M.Sc. in Computer Science, Electrical Engineering, or a related field. Candidates with a B.Sc. and at least 2 years of relevant experience in developing computer vision or machine learning algorithms will also be considered.

• Significant experience* in computer vision approaches with proven track‑record of developing algorithms, documentation, and production‑quality software code for image analysis. Experience*** with biological or agricultural images is an asset.
• Strong proficiency** in Python and experience*** with C++ for performance‑critical computer vision applications.
• Experience*** in computational geometry, 3D representations and related data structures.
• Experience*** developing and training deep learning models for computer vision, including convolutional neural networks (CNNs), vision transformers, and foundation models.
• Experience*** mentoring junior developers, engineers, or students in machine learning or computer vision methods and software development best practices.
• Experience*** in parallel processing (e.g., across GPUs) and multi‑threaded application design is preferred.
• Experience*** with transfer learning techniques to leverage pre‑trained models, including large foundation models, will be considered an asset.

* Significant experience is defined as 3+ years of directly relevant experience, with demonstrated application across multiple projects.
** Strong proficiency is defined as 3+ years of experience, with demonstrated ability to independently develop, debug, and maintain code across multiple projects.
*** Experience is defined as 2+ years of hands‑on experience, with demonstrated application in one or more projects.

Condition of employment

Reliability Status

For a Reliability Status, verification of background information over a period of 5 years is required.

Candidates will be assessed on the basis of the following criteria:

• Knowledge of classical computer‑vision methods including pre‑processing, segmentation, object detection and classification including foundational theory and familiarity with OpenCV or similar frameworks.
• Knowledge of 3D reconstruction techniques and generation of 3D representations such as point clouds or meshes.
• Understanding of deep learning methods for computer vision and their application to image analysis.
• Proficiency in training dataset preparation, including preprocessing, augmentation, and annotation, and familiarity with self‑supervised or representation learning approaches.
• Proficiency in modern deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with model evaluation, validation, and performance metrics for computer vision tasks.
• Ability to use version control systems (e.g., Git) and workflow or pipeline tools to support reproducible machine learning experiments and data processing (e.g., Snakemake, MLflow, or similar).
• Practical knowledge of open‑source frameworks for federated learning will be considered an asset.
• Technology support - Conceptual and analytical ability (Level 3)
• Technology support - Self‑knowing and self‑development (Level 3)

Competency Profile(s)

For this position, the NRC will evaluate candidates using the following competency profile(s): Technology Support

NRC employees enjoy a wide-range of competitive benefits including a robust pension plan, comprehensive health and dental coverage, disability and life insurance, office closure at the end of December, and additional supports to enhance your well‑being throughout your career and beyond.

Notes

• In 2025, the NRC was chosen as one of Canada’s Top Employers for Young People, a National Capital Region Top Employerand Forbes Canada’s Best Employer.
• Relocation assistance will be determined in accordance with the NRC's directives.
• A pre-qualified listmay be established for similar positions for a one year period.
• Preference will be given to Canadian Citizens and Permanent Residents of Canada. Please include citizenship information in your application.
• The incumbent must adhere to safe workplace practices at all times.
• We thank all those who apply, however only those selected for further consideration will be contacted.

Please direct your questions, with therequisition number (25241) to:

Closing Date: 22 September 2026 - 23:59 Eastern Time