Senior Applied Researcher AI/ML ( CAD)

For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings. For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role. The Role

Reporting to the Vice President, Advanced Technology, you will be a member of the Advanced Technology / Applied AI Research team at PointClickCare. You are a talented Senior Applied Researcher in AI / Machine Learning, looking to make a difference solving critical customer and business challenges in the dynamic healthcare market. You thrive applying creative and pragmatic solutions—from framing problems and preparing data, to selecting appropriate techniques, building and evaluating model systems—that will truly transform healthcare and enable our customers to deliver exceptional care. Responsibilities

• Apply machine learning and AI techniques including GenAI and LLM-based approaches to develop model systems and solutions, collaborating across functions to scale and integrate these into large-scale cloud-based SaaS production environments for healthcare. - Work with product leaders, clinical informaticists, data scientists, UI/UX researchers, engineers, and healthcare professionals to design and deliver high value solutions. - Design, build and evaluate solutions that may involve structured or unstructured data for healthcare use cases, delivering capabilities such as predictive models, summarization, recommenders, semantic search, extraction, classification, or other AI/ML applications

• Perform research and experimentation to select appropriate approaches, algorithms, and evaluation methods, and execute the R&D needed to deliver production-ready model systems. - Perform data collection, cleaning, analysis, prompt tuning, parameter fine-tuning, model training, development, and evaluation—using existing or developing new tools or workflows as needed. - Apply and help evolve responsible AI evaluation practices, using approaches and frameworks such as LLM-as-judge, RAGAS, and LangSmith, and design and implement custom model performance and quality metrics as needed to ensure model quality, fairness, and safety at scale. - Contribute to a collaborative team culture, sharing knowledge and helping develop new capabilities across the organization. Required Experience

• Ph.D grad, with 1+ years related industry experience OR Masters grad with 4+ years industry experience--including at least 1 year directly related to healthcare ML/AI

• Proven industry experience through multiple major product releases in a commercial SaaS environment. - Hands-on experience working with healthcare data (e.g. EHR, ADT, clinical notes). - Proficiency in Python. Proficiency in Java or other languages helpful. - Proficiency with SQL and data engineering for AI/ML applications. - Experience working with large datasets using big data frameworks (e.g. Azure Data Lake, Apache Spark or Databricks)

• Solid understanding of transformer models and LLM-based approaches, including hands-on experience with prompt tuning and PEFT methods (e.g. LoRA, QLoRA) using frameworks such as Hugging Face Transformers. - Experience building and evaluating models using modern ML packages such as NumPy, SciPy, Pandas, Scikit-learn, PyTorch, and LightGBM. - Experience building and deploying models using public cloud infrastructure (Azure, AWS, or Google Cloud), including familiarity with version control, CI/CD pipelines, and scaling considerations for production ML systems at SaaS scale. - Strong communication and collaboration skills; comfortable working on a distributed team. - Experience with one or more of: reinforcement learning or RLHF, NLP techniques for summarization, extraction, classification, or semantic search, retrieval-augmented generation (RAG) pipelines and/or agentic frameworks (e.g. LangChain, LlamaIndex).