Senior Applied Researcher – AI/ML (CAD)

About the role

Reporting to the Vice President of Advanced Technology, you will join the Applied AI Research team at PointClickCare. This senior position focuses on creating AI/ML solutions that transform healthcare delivery for our customers.

Key responsibilities

• Apply machine learning and AI techniques, including GenAI and LLM‑based approaches, to develop model systems and scale them within large‑scale cloud SaaS environments for healthcare.
• Collaborate 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 using structured or unstructured healthcare data, delivering predictive models, summarization, recommenders, semantic search, extraction, classification, and other AI/ML applications.
• Conduct research and experimentation to select appropriate algorithms and evaluation methods, executing R&D to deliver production‑ready model systems.
• Perform data collection, cleaning, analysis, prompt tuning, parameter fine‑tuning, model training, development, and evaluation using existing or new tools and workflows.
• Apply and evolve responsible AI evaluation practices using frameworks such as LLM‑as‑judge, RAGAS, and LangSmith, and design custom performance and quality metrics to ensure fairness and safety at scale.
• Contribute to a collaborative team culture by sharing knowledge and helping develop new capabilities across the organization.

Required profile

• Ph.D. graduate with 1+ year industry experience or Master’s graduate with 4+ years, including at least 1 year in healthcare ML/AI.
• Proven experience delivering multiple major product releases in a commercial SaaS environment.
• Hands‑on experience with healthcare data such as EHR, ADT, and clinical notes.
• Proficiency in Python; knowledge of Java or other languages is a plus.
• Strong SQL skills and data‑engineering experience for AI/ML pipelines.
• Experience with large‑scale data platforms (Azure Data Lake, Apache Spark, Databricks).
• Deep understanding of transformer models and LLM‑based approaches, including prompt tuning and PEFT methods (LoRA, QLoRA) using Hugging Face Transformers.
• Experience building and evaluating models with NumPy, SciPy, Pandas, Scikit‑learn, PyTorch, and LightGBM.
• Experience deploying models on public cloud (Azure, AWS, or Google Cloud) with version control, CI/CD pipelines, and scaling considerations for SaaS‑scale production.
• Strong communication and collaboration skills in distributed teams.
• Familiarity with reinforcement learning or RLHF, NLP techniques for summarization, extraction, classification, semantic search, retrieval‑augmented generation (RAG) pipelines, and agentic frameworks such as LangChain or LlamaIndex.