Machine Learning Resident - Client: Hines Health Services Inc. term
Job Description & How to Apply Below
“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…
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