Machine Learning Resident – Client Theragraph (4.5 month term)

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.

Abdul Wahab Machine Learning Scientist Advanced Technology

This is a paid residency that will be undertaken over a 4.5-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.

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.

This project involves the development of an intelligent data extraction pipeline from unstructured data source to structure complex medical records for Crohns 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.

Are you passionate about building great solutions Youll be presented with opportunities to both personally and professionally develop as you build your career. Were 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.)

• 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 models general reasoning or formatting 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 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.
• Experience training/fine-tuning LLMs/MLLMs using RL algorithms like PPO GRPO etc.
• 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.

• Experience working with medical datasets and/or in the healthcare domain.
• Experience processing structured and unstructured data across a variety of formats.
• Hands-on experience with cloud platforms such as AWS.
• Publications in peer-reviewed machine learning conferences or relevant journals.
• Familiarity with software engineering best practices.
• Experience deploying machine learning models to production or strong software engineering / MLE skills is an asset.

• Takes ownership of problems and demonstrates initiative and leadership in moving work forward.
• Interdisciplinary team player enthusiastic about working together to achieve excellence.
• Brings critical thinking independent thought and sound technical judgment to complex problems.
• Communicates technical concepts clearly and can help guide the effective application of machine intelligence.
• Can assess prepared datasets for potential bias in human feedback before model training begins.
• Focuses on reducing the error rate in the final data not just exploring models or architectural research.
• Brings intellectual curiosity and the desire to learn new methods tools and technologies.

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 clients organization at the end of the term (at the clients discretion)

One of Canadas 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 worlds 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.

If this sounds like the opportunity youve been waiting for please dont wait for the closing date October 9 2026 to apply were 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 youd be a fit for 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 wont be used in the selection process.