“If you are excited by the challenge of using ML to make sense of large, complex and disparate health datasets, this is the right opportunity for you. Be a part of a collaborative team of clinicians, scientists and engineers building a real world solution that aims to bring clarity and efficiency to clinical practice.”

• Dave Staszak, Lead Machine Learning Scientist, Advanced Technology

Description About the Role

This is a paid residency that will be undertaken over a 6-month period with the potential to be hired by our client, Theragraph, afterwards (note: at the discretion of the client). The Resident will report to an Amii Scientist and regularly consult with the client team to share insights and engage in knowledge transfer activities. Successful candidates will be members of a cross-functional project team with backgrounds in ML research, project management, software engineering, and new product development. This is a rare opportunity to be mentored by world-class scientists and to develop something truly impactful.

About The Client Theragraph is an early stage digital health startup based in Edmonton, Alberta. We simplify the complexity of health records to help specialist physicians make better decisions in less time. At the same time, we provide patients with never-before accessible visibility into their disease history and pharmaceutical companies with the data they need to innovate. Founded and led by practicing physicians, Theragraph is built to be overwhelmingly useful for healthcare providers and a bridge for harnessing health data towards better health outcomes. We are a small, scrappy team of intelligent, pro-active and curious people. We are currently fully remote but like to gather in person when we can.

About The Project This project involves the development of an intelligent data extraction pipeline from unstructured data source to structure complex medical records for Crohn’s and Ulcerative Colitis patients. We utilize an open-source 20B parameter GPT model to process unstructured OCR text into high-fidelity clinical datasets, supported by a robust two-stage Human-in-the-Loop (HITL) framework. This system captures real-time expert corrections for both noisy machine-generated text and LLM structural errors, generating a rich repository of preference pairs and audit logs. The primary objective is to apply advanced Reinforcement Learning techniques (such as RLHF or GRPO) to fine-tune the LLM, enabling the model to learn directly from clinical subject matter expert

evaluations. Built on a scalable AWS architecture, the project focuses on minimizing model hallucinations and managing the "alignment tax" to ensure precise medical reasoning and formatting. By closing the feedback loop between human validation and model training, we aim to achieve production-grade accuracy in the automated capture of specialized health data.

Required Skills / Expertise Are you passionate about building great solutions? You’ll be presented with opportunities to both personally and professionally develop as you build your career. We’re looking for a talented and enthusiastic individual with a solid background in machine learning, specifically human-in-the-loop reinforcement learning techniques, ideally with some experience with unstructured data extraction methods (OCR-, LLM-based, etc.).

Key Responsibilities

• Design, implement, optimize, and evaluate models to accurately capture health data across a range of data types, formats, and domains.
• Apply advanced reinforcement learning methods (e.g., RLHF, GRPO) and subject matter expert evaluations to learn from mistakes and improve data collection processes.
• Conduct applied research on reinforcement learning, OCR and RAG techniques to overcome limitations in current approaches.
• Optimize ML pipelines to ensure efficiency, scalability, and real-time processing capabilities.
• Implement specific metrics to measure "alignment tax" and ensure that RLHF improvements in accuracy do not degrade the model’s general reasoning or formatting capabilities.
• Collaborate with the project team and stakeholders to develop MVP and client focused solutions.

Required Qualifications

• Completion of a Computer Science (or a related scientific graduate degree program) MSc. or PhD with a relevant specialization like health data or reinforcement learning.
• 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 ML frameworks, libraries, and toolkits (e.g., Scikit-learn, PyTorch, OpenCV, 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.

Preferred Qualifications

• Familiarity with and hands-on experience with a variety of unstructured and structured data.
• Hands-On Experience with Cloud Platforms like AWS.
• Publication record in peer-reviewed academic conferences or relevant journals in machine learning.
• Experience/familiarity with software engineering best practices.
• Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus.

Non-Technical Requirements

• Desire to take ownership of a problem and demonstrated leadership skills.
• Interdisciplinary team player enthusiastic about working together to achieve excellence.
• Capable of critical and independent thought.
• Able to communicate technical concepts clearly and advise on the application of machine intelligence.
• Able to analyze the "already prepared" dataset to identify biases in human feedback before training begins.
• Focused on reducing the error rate in the final data rather than just architectural research.
• Intellectual curiosity and the desire to learn new things, techniques, and technologies.

Why You Should Apply

Besides Gaining Industry Experience, Additional Perks Include

• 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)

About Amii One of Canada’s three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the world’s top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.

How to Apply

If this sounds like the opportunity you've been waiting for, please don’t wait for the closing March 6, 2026 to apply - we’re excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and won’t be used in the selection process.