Intern, AI & ML Researcher

Kinaxis

. Conduct applied machine learning research focused on time-series forecasting and generative AI for supply chain planning .

Posted 10/2/2026full-timeCanadaEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong expertise in machine learning, statistics, and experimental methodology, with hands-on experience in applied research, particularly in time-series forecasting and generative AI. Proficient in Python and modern machine learning frameworks, capable of translating complex problems into actionable research questions and experiments.

Highest-signal resume keywords

Machine LearningTime-Series ForecastingPython ProgrammingGenerative AIStatistical Inference

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatisticsExperimental MethodologyTime-Series ForecastingGenerative AIPython ProgrammingProbabilistic ForecastingCausal InferenceFoundation Models EvaluationResearch Communication

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

Machine Learning Frameworks

Industry Keywords

Applied MathematicsOperations ResearchResearch PrototypesTechnical ReportsPublications

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Conduct applied machine learning research focused on time-series forecasting and generative AI for supply chain planning
Review literature, identify promising methods, and formulate research questions and hypotheses
Design and run reproducible experiments using real-world and benchmark datasets
Develop and evaluate models against strong baselines using quantitative and qualitative metrics
Analyze results, limitations, robustness, and practical trade-offs
Communicate findings to technical and non-technical audiences
Build research prototypes and collaborate with researchers, data scientists, engineers, and domain experts to assess product potential
Contribute to product ideas, technical reports, demonstrations, publications, and patent applications where appropriate
Work with a dedicated mentor throughout the internship

Requirements

What you’ll need

Currently enrolled in or recently graduated from a master's or PhD program in Computer Science, Machine Learning, Statistics, Applied Mathematics, Operations Research, or a related field
Eligible for an Intern position: currently enrolled in full-time education or, if a recent/upcoming graduate, graduation date within 12 months of the placement end date
Strong foundation in machine learning, statistics, and experimental methodology
Hands-on AI/ML research experience through graduate research, publications, thesis work, research internships, or substantial projects
Proficient in Python and experienced with modern machine learning frameworks
Ability to translate open-ended problems into testable hypotheses, well-designed experiments, and clearly supported conclusions
Research experience in time-series forecasting, probabilistic forecasting, and time-series foundation models including PFNs (nice to have)
Experience with generative AI, including large language models, retrieval-augmented generation, and agents (nice to have)
Knowledge of statistical inference, uncertainty quantification, and causal inference (nice to have)
Experience evaluating foundation models, including fine-tuning, adaptation, benchmarking, and error analysis (nice to have)
Research communication through publications, preprints, or technical reports (nice to have)
Must be located in the Eastern time zone of Canada; Ottawa or Toronto-based interns must work from the office at least three days a week

Benefits

Comp & perks

Flexible vacation and Kinaxis Days (company-wide days off)
Flexible work options
Physical and mental well-being programs
Regularly scheduled virtual fitness classes
Mentorship programs, training, and career development
Recognition programs and referral rewards
Hackathons