Machine Learning Resident - Client: Hines Health Services Inc. (6 month term)

“If you are interested in the application of ML and LLMs to innovate in job matching and medical staffing, this is the right opportunity for you. Be a part of a collaborative team of domain experts, machine learning scientists and engineers building a real world application from the ground up and get mentored by some of the best minds in AI during the process.” 

- 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, Hines Health Services, 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 & Platform Context

Hines Health Services (HHS) delivers mission-critical occupational health, emergency medical services, and medical staffing solutions for industrial and government clients operating in high-risk, regulated environments. HHS is purpose-built to scale domestically across Canada, while deliberately laying the foundation for international expansion into global markets, including the Kingdom of Saudi Arabia.

A core differentiator is mobilization at scale, anchored in Canada and extendable globally. HHS currently maintains a network of 1,800 vetted and credentialed medical professionals, with an average weekly onboarding rate of 100 new professionals. This growing workforce enables rapid deployment of compliant medical teams, mobile clinics, and on-site services while preserving consistent clinical governance, credential verification, and audit-ready reporting.

This capability is enabled by Hines Automated Recruitment Platform (HARP), HHS’s proprietary recruitment and workforce intelligence platform. HARP supports scalable mobilization by continuously matching client requirements with credentialed talent, tracking readiness in real time, and providing data-driven insights that reduce deployment risk, accelerate time to site, and support controlled expansion from Canadian operations into global markets.

About the Project

Vision Build Canada’s leading AI-powered medical workforce platform and expand it globally, enabling rapid, compliant mobilization of credentialed medical professionals across high-risk environments worldwide.

Project Overview This project advances the Hines Automated Recruitment Platform (HARP) into a scalable, AI-driven workforce intelligence system supporting mission-critical medical staffing for industrial, government, and remote operations. The initial focus is Canada, where Hines Health Services maintains a network of approximately 1,800 vetted and credentialed medical professionals, growing by an average of 100 new professionals per week, with the platform deliberately architected to support future global deployment.

Continuous Improvement and Platform Evolution The project is guided by a continuous improvement mindset. Machine learning models, matching logic, and data pipelines are iteratively refined based on real-world performance, recruiter feedback, and deployment outcomes. A human-in-the-loop design ensures that algorithmic recommendations are continuously validated against clinical judgment and operational realities, enabling measured improvement without compromising safety, compliance, or transparency.

Project Objectives The Machine Learning Resident will contribute to:

• Enhancing AI-driven candidate-job matching across credentials, experience, availability, and regulatory requirements.


• Improving model performance, scalability, and robustness through iterative testing and evaluation.


• Strengthening data pipelines and feature engineering to support mobilization at scale.


• Supporting production-ready ML solutions that integrate into HHS’s live operational environment.

Why This Matters In high-risk healthcare environments, trust is built through consistent performance, not promises. By embedding continuous improvement into the platform’s design, this project ensures HARP evolves alongside client needs, regulatory expectations, and workforce dynamics, reinforcing HHS’s ability to deliver reliable medical staffing at scale.

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 NLP and LLM techniques. 

Key Responsibilities:

• Design, implement, optimize, and evaluate models to perform NLP tasks (summarization, information extraction, semantic comparisons, etc.)


• Prepare, curate, and preprocess high-quality datasets for training and validating models.


• Utilize state-of-the-art LLM and ML frameworks, tools and open-source libraries to enhance model performance, accelerate workflows, and optimize data processing.


• Undertake applied research on ML and LLM techniques to address the limitations in 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.


• Support the productionalization and deployment of models in client environment

Required Qualifications: 

• Completion of a Computer Science (or a related scientific graduate degree program) MSc. or PhD.


• Research or project experience in machine learning, specifically using NLP/LLM tools and techniques


• Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, TensorFlow, PyTorch, OpenCV, Pandas, HuggingFace, Langchain, Llamaindex ).


• 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 unstructured data.


• Publication record in peer-reviewed academic conferences or relevant journals in machine learning (specifically LLM work or applied ML applications)


• 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 demonstrate 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.


• 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 date of March 4, 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 and the role. 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.