Design, implement, optimize, and evaluate models to accurately extract and collate scientific data from public and private sources.
Prepare, curate, and preprocess unstructured data into high-quality datasets to then use for predictive model training, fine-tuning, and validating.
Utilize state-of-the-art LLM and ML frameworks, tools and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing.
Conduct applied research on LLM and ML techniques, with a focus to understanding and addressing the limitations of existing models.
Optimize ML pipelines to ensure efficiency, scalability, and real-time 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 developing and training, fine-tuning and evaluating machine learning and deep neural network models in PyTorch and/or TensorFlow.
Proficient in Python programming language and related LLM and ML frameworks, libraries, and toolkits (e.g., Scikit-learn, PyTorch, Pandas, HuggingFace, LangGraph).
Solid understanding of classical statistics and its application in 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 – 1 year term

Key skills