AI/ML Researcher for Autonomous Aerial Systems

Discover the possible

Anything is possible at the NRC, named in 2025 one of Canada’s Top Employers for Young People, Top Employer in the National Capital Region and Forbes Canada’s Best Employers!

As Canada’s largest research and innovation organization, our world-renowned research pushes the boundaries of science and engineering to make the impossible, possible. Every day we explore new ideas through innovative research and help companies discover possibilities that impact Canada’s future and the world.

At the NRC, you’ll also discover new possibilities. Our supportive workplace fosters a culture of creativity, welcoming fresh perspectives and innovation at all levels. We value teamwork. You’ll collaborate across multiple fields and with the brightest minds to find creative solutions. Most importantly, you’ll discover what’s possible within you as you grow, make valuable contributions and progress in your professional journey. From ground-breaking discoveries to a life-changing career, discover your possible at the NRC.

The role

We are looking for an AI/ML core algorithms researcher who can develop foundational machine learning algorithms that enable next-generation autonomous aerial systems. The successful candidate will take a leading role in researching and developing core AI/ML algorithms for autonomous decision-making, efficient onboard intelligence, and adaptive learning in unmanned aerial systems within the National Research Council of Canada (NRC) Aerospace Research Centre’s Drone and Flight Autonomy Lab. In this role, the candidate will collaborate closely with specialists in flight controls and sensor integration while maintaining a primary focus on fundamental algorithmic innovation.

This individual will share and demonstrate NRC’s core values of Integrity, Excellence, Respect, and Creativity.

The Centre for Drone Innovation, part of the NRC’s Drone and Flight Autonomy Lab, strengthens Canada’s ability to research, develop, test and validate advanced drone technologies. The Centre serves as a national hub supporting all stages of drone innovation—from design and simulation to prototype development, testing and qualification.

Facilities include:

• Drone hangar and operations centre with direct runway access;
• Technical laboratories;
• Indoor and outdoor flight test arenas;
• Secure research spaces for specialized projects. 

Working with a multidisciplinary team of researchers, engineers, and partners, the Research Officer will:

• Developing novel deep learning architectures specifically designed for sequential decision-making, Temporal representation learning, Uncertainty-aware and probabilistic methods
• Researching efficient inference algorithms and neural network optimization methods for resource-constrained embedded platforms (edge AI);
• Creating core algorithms for reinforcement learning, imitation learning, and meta-learning with application to adaptive flight control and navigation;
• Designing distributed learning algorithms for multi-agent coordination and federated learning across swarms of aerial vehicles;
• Developing novel optimization algorithms for real-time adaptation and continual learning in dynamic, safety-critical environments;
• Publish in leading venues (e.g., NeurIPS, ICML, ICLR, AAAI) and contribute to open-source research artifacts

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

PhD from a recognized university specializing in Computer Science, Machine Learning, Statistics, or Applied Mathematics with specialization in core AI/ML algorithms, optimization, or computational learning theory. 

For information on certificates and diplomas issued abroad, please see Degree equivalency

Experience

• Demonstrated experience in developing novel machine learning algorithms and models, supported by peer-reviewed publications or equivalent research contributions.
• Significant experience applying statistical learning theory, optimization, and probabilistic modelling, with the ability to apply these principles to new algorithm design.
• Significant experience working in at least one of the following areas: sequential or time-series modelling, reinforcement learning, generative modelling, or representation learning.
• Hands-on experience implementing ML algorithms from first principles using Python, C++, CUDA, or Julia—including custom autodiff, kernel optimizations, or training loop implementations;
• Experience across the full spectrum of research practice, including identifying research requirements, developing proposals, managing projects, collecting and analyzing data, ensuring quality assurance, and disseminating results through technical reports, presentations, and peer-reviewed publications.
• Experience with embedded or resource-constrained ML (model compression, quantization, neural architecture search) is considered an asset;
• Experience with aerial robotics, autonomous systems, or safety-critical ML applications is considered an asset.
• Experience with distributed training or multi-agent learning algorithms is considered an asset.

Condition of employment

Secret clearance

A thorough security clearance process will be applied. 

For a Secret Clearance, verification of background information over a period of 10 years is required. Individuals must have lived in Canada for a sufficient period of time to enable the security screening process. 

Language requirements

English  

Information on language requirements and self-assessment tests

Assessment criteria

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

Technical competencies

• Advanced knowledge of statistical learning theory, generalization, and model evaluation principles.
• Advanced knowledge in building Convolutional Neural Network (CNN) architecture (such as ResNet and VGG) and transfer learning methods (supervised and unsupervised).
• Advanced knowledge of optimization methods used in machine learning, including stochastic gradient-based and non-convex optimization techniques.
• Advanced abilities in designing novel machine learning algorithms and model architectures from first principles.
• Advanced abilities applying probabilistic modelling and uncertainty quantification, including Bayesian approaches and stochastic methods.
• Advanced knowledge of sequential and time-series modelling frameworks, including state-space models and modern deep learning approaches.
• Ability to analyze algorithmic complexity, scalability, and performance trade-offs in high-dimensional settings.
• Ability to perform experimental design, benchmarking, and reproducible research practices for validating new methods.
• Advanced abilities in implementing algorithms using modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) with an emphasis on flexibility for research experimentation.
• Ability to with handle large-scale and streaming datasets, including efficient data processing and online learning paradigms.
• Ability to bridge theoretical insights with empirical validation, ensuring robustness and practical relevance of proposed methods.

Behavioural competencies

Competency Profile(s)

For this position, the NRC will evaluate candidates using the following competency profile: Research

View all competency profiles

Compensation

This position is classified as a Research Officer (RO), a group that is unique to the NRC. Candidates are remunerated based on their expertise, outcomes and impacts of their previous work experience relative to the requirements of the level. The salary scale for this group is vast, from $64,739 to $183,010 per annum, which permits for employees of all levels from new graduates to world-renowned experts to be fairly compensated for their contributions.

NOTE: Please note that the full RO/RCO salary scale has five levels. Salary determination will be based on a review of the candidate’s expertise, outcomes and impacts of their previous work experience relative to the requirements of the level. As such, the initial salary could be within another level of the RO/RCO salary scale (i.e., above or below the intended level for this position).

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 Employer and Forbes Canada’s Best Employer.
• Relocation assistance will be determined in accordance with the NRC's directives.
• A pre-qualified list may 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.