Intern AI&ML Researcher

Role and team

Location and work arrangement

• Candidates must be located in the Eastern time zone.
• Candidates in Ottawa or Toronto must work from the office at least three days per week; other Eastern time zone locations in Canada are remote.

Qualifications

• Applicants must be 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.
• Recent or upcoming graduates must graduate within 12 months of the placement end date.
• Candidates need a strong foundation in machine learning, statistics, and experimental methodology; hands-on AI/ML research experience; Python proficiency; and experience with modern machine learning frameworks.
• Candidates should be able to formulate testable hypotheses and well-designed experiments, communicate technical ideas, and collaborate across research, engineering, and business.
• Time-series forecasting, probabilistic forecasting, generative AI, LLMs, RAG, agents, statistical inference, uncertainty quantification, causal inference, and foundation-model evaluation are nice to have.
• Publications, preprints, or technical reports are a plus.

Research responsibilities

• Conduct applied machine learning research in time-series forecasting and generative AI for supply chain planning.
• Review literature, identify methods, formulate research questions, and design reproducible experiments with 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, and communicate findings to technical and non-technical audiences.
• Build research prototypes and collaborate with research, data science, engineering, 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 who provides research guidance and support.

Technologies

• Tools include Docker, Python and scientific computing libraries, PyTorch and deep learning frameworks, experiment tracking, TensorBoard, version control, and cloud-based compute.

Benefits

• Benefits may vary by location and employee and include flexible vacation, company-wide days off, flexible work options, well-being programs, mentorship, training, career development, recognition programs, referral rewards, and hackathons.