Design, implement, and optimize a multi-level evaluation framework for agentic systems.
Generate simulated datasets, robust metrics, and reproducible test methodologies to evaluate agentic and sequential decision behaviors under a variety of environments.
Undertake applied research on ML and statistical techniques to address the limitations in existing models and approaches.
Optimize ML and evaluation pipelines to ensure efficiency and scalability processing capabilities.
Collaborate with the project team and stakeholders to develop MVP and client focused solutions.
Engage in regular client meetings, contributing to presentations and reports on project progress.

Completion of a Computer Science (or a related scientific/engineering graduate degree program) MSc. or PhD.
Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, PyTorch, Gym/Gymnasium, OpenCV, HuggingFace).
Solid understanding of classical statistics and its application in experiment design and model validation.
Familiarity with Linux, Git version control, and writing clean code.
A positive attitude towards learning and understanding a new applied domain.
Must be legally eligible to work in Canada.

Work under the mentorship of an Amii Scientist for the duration of the project
Participate in professional development activities
Gain access to the Amii community and events
Get paid for your work (a fair and equitable rate of pay will be negotiated at the time of offer)
Build your professional network
The opportunity for an ongoing machine learning role at the client’s organization at the end of the term (at the client’s discretion)

Machine Learning Resident – Client: Artificial Agency

Key skills