Kinaxis
Kinaxis
Kinaxis develops supply chain orchestration software that helps organizations plan, manage, and respond to complex operations. Its Maestro platform combines AI-infused planning, advanced analytics, and real-time data visibility to support more informed decisions across the supply chain. The company’s solutions are relevant to organizations in areas including manufacturing, healthcare, and consulting, with a focus on improving efficiency, anticipating demand, and adapting quickly when disruptions occur.

AI and Machine Learning Research Intern – Canada (Hybrid)

Research time-series forecasting and generative AI methods for supply chain planning at Kinaxis. Develop practical machine-learning capabilities through experiments, prototypes, and collaboration with research and product teams.

Description

  • Conduct applied machine-learning research in time-series forecasting and generative AI for supply chain planning
  • Review research literature, identify promising approaches, and define testable questions and hypotheses
  • Design and execute reproducible experiments with real-world and benchmark datasets
  • Build and assess models against strong baselines using quantitative and qualitative measures
  • Interpret findings, limitations, robustness, and practical trade-offs
  • Present research outcomes to both technical and non-technical audiences
  • Create research prototypes and work with researchers, data scientists, engineers, and domain experts to evaluate product potential
  • Contribute to product concepts, technical reports, demonstrations, publications, and patent applications when appropriate
  • Work with a dedicated mentor throughout the internship

Requirements

  • Currently pursuing or recently completed a master's or PhD in computer science, machine learning, statistics, applied mathematics, operations research, or a related discipline
  • Eligible for an internship through current full-time enrollment or graduation within 12 months of the placement end date
  • Strong grounding in machine learning, statistics, and experimental design
  • Practical AI/ML research experience from graduate work, publications, a thesis, research internships, or substantial projects
  • Proficiency in Python and experience with contemporary machine-learning frameworks
  • Able to turn open-ended problems into testable hypotheses, rigorous experiments, and well-supported conclusions
  • Experience with time-series forecasting, probabilistic forecasting, or time-series foundation models such as PFNs is an advantage
  • Familiarity with generative AI, including large language models, retrieval-augmented generation, and agents, is an advantage
  • Knowledge of statistical inference, uncertainty quantification, or causal inference is an advantage
  • Experience evaluating foundation models through fine-tuning, adaptation, benchmarking, or error analysis is an advantage
  • Research communication experience demonstrated through publications, preprints, or technical reports is an advantage
  • Must be based in Canada's Eastern time zone; interns in Ottawa or Toronto must work from the office at least three days per week

Benefits

  • Flexible vacation policy and company-wide Kinaxis Days off
  • Flexible working arrangements
  • Programs supporting physical and mental well-being
  • Regular virtual fitness classes
  • Mentorship, training, and career-development opportunities
  • Recognition programs and referral rewards
  • Hackathons

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