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At PointClickCare our mission is simple: to help providers deliver exceptional care. And that starts with our people. As a leading health tech company that’s founder-led and privately held, we empower our employees to push boundaries, innovate, and shape the future of healthcare. With the largest long-term and post-acute care dataset and a Marketplace of 400+ integrated partners, our platform serves over 30,000 provider organizations, making a real difference in millions of lives. We also reinvest a significant percentage of our revenue back into research and development, ensuring our employees have the resources to innovate and make a lasting impact. Recognized by Forbes as a top private cloud company and honored as one of Canada’s Most Admired Corporate Cultures, we offer flexibility, growth opportunities, and meaningful work. At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change. Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making. By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact. It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey. Join us in redefining healthcare — so it doesn’t just survive, it thrives.
Job Responsibility:
You will be applying NLP including GenAI and other AI/ML techniques to develop model systems and solutions, collaborating across functions to scale and integrate advanced solutions into successful end user experiences in large-scale cloud based SaaS production environments for healthcare
You will be working with product leaders, clinical informaticists, data scientists, UI/UX researchers and designers, other AI and machine learning and domain experts, engineering teams and others, including work with customers and users who are healthcare professionals
Design, build and evaluate solutions that may involve structured or unstructured data including speech or natural language for healthcare use cases, delivering capabilities such as summarization, predictive models, recommenders, semantic search, extraction, classification or other NLP, AI or machine learning based techniques
You will be performing research and experimentation to select appropriate approaches, algorithms, evaluation methods and frameworks and doing the R&D to deliver model systems
You will perform, oversee and assist in data collection, data cleaning, data analysis, algorithm selection or design, prompt tuning, parameter fine tuning, training, development and evaluation of systems that deliver responsible AI solutions at scale, using existing or developing new tools or workflows as needed
As a principal applied researcher, you will bring deep technical expertise and also provide mentorship on advanced AI, NLP, data science, statistical and machine learning methods and technologies, helping the organization develop new capabilities for innovative solutions
You will have substantial independence and responsibility from day one
Requirements:
PhD or comparable level of experience in Computer Science, Math, Physics, Engineering or a related field
4-10+ year industry experience building solutions in commercial SaaS, including at least 4 years working in applications of NLP, Search or AI/ML technologies for healthcare
Strong interest in applying AI/ML/NLP to healthcare related problems and data
Expert-level practical, hands-on experience developing and applying a wide range of techniques in Natural Language Processing, including fine tuning of LLMs and other Transformer models, plus one or more additional AI/ML or Search related areas of expertise to solve real-world problems at scale
Demonstrated ability to lead and perform research and experimentation to select appropriate approaches, algorithms, evaluation methods, and frameworks, as well as tasks such as feature selection, language modeling, evaluation and fine tuning or training models, applying standard approaches or developing new tools or workflows as needed to meet project requirements
Significant experience building and deploying AI/machine learning and NLP models for large-scale SaaS products, including familiarity with industry standard software development concepts such as scaling issues, version control, CI/CD pipelines, and security
Solid understanding and experience with transformer models and multiple kinds of NLP and ML models and approaches including logistic regression, random forest, ensemble methods, SVM, KNN, reinforcement learning, and other ML techniques
Proficiency in Python and Java required. Proficiency in JavaScript or TypeScript and modern UI frameworks for building prototype or tool front ends desired
Proficiency doing data engineering for ML and NLP applications, including exposure to database systems and proficiency with SQL
Proficiency building models from big data using modern packages, models and data analysis stacks such as NumPy, SciPy, Pandas, Scikit-learn, PyTorch, Keras, LightGBM, fastText, NLTK, and spaCy. Proficiency fine tuning Hugging Face Transformers required
Accomplished and curious problem finder and problem solver, able to think both creatively and methodically, including strong fundamentals in optimization, problem solving, model building and evaluation
Experience working with large data sets using big data processing frameworks (e.g. Azure Data Lake, Apache Spark or other cluster computing/MapReduce frameworks)
Happy doing whatever data wrangling and cleaning are necessary to create solutions, while also finding ways to make model development and evaluation processes more efficient and scalable
Extensive experience using public cloud infrastructure for building, evaluating and deploying machine learning models (Azure, AWS, Google Cloud)
Exceptional communication and collaboration skills, including extensive experience working across all organizational levels and functions with both internal and external stakeholders, and comfortable working on a distributed team
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