About the Role
PointClickCare’s Advanced Technology / AI Applied Research team designs, builds, tunes, evaluates, and delivers AI model systems on clinical and operational data to help providers deliver excellent care. The Senior Applied Research Engineer ensures we have data in the right shape to develop and deliver that AI safely, effectively, and significantly more efficiently than today.
You will build and own the gold data layer that sits between our silver Lakehouse data and the AI work that depends on it--building it, validating it, documenting it, and extending it as products evolve and new AI needs come into scope. What you build will be a highly leveraged asset, relied on by multiple AI model system creators across the full R&D lifecycle: EDA, experiments, model development, evaluation, and operational sustaining--supporting AI work ranging from classical ML to the latest generative and agentic approaches.
This role blends data engineering with applied AI data science. You will sit with AI researchers to understand what they need, and work with data platform, product, clinical, and workflow experts to understand the data, where it comes from, it’s transformation from raw to silver, and what it means. This is the first hire in a function expected to grow over time, embedded directly in PCC’s team of AI model development experts.
What You’ll Do
Own the gold data layer. Transform messy, silver tables into curated, semantically rich, clean and documented gold datasets suitable for AI model development, including datasets and features reusable for AI development across projects. Maintain the data as products and needs evolve. To do this you will
Reverse-engineer data semantics. Talk with product engineers, clinical and workflow experts to learn how the products are used and how data are created in the field. Understand SQL queries, stored procedures, technical data definitions, and other code to know how products represent and transform data. Learn how data are ingested into the data lake, what silver tables and columns actually represent and how they behave. Capture provenance, semantics, clinical event sequencing, cross module record linkage and known quirks.
Bridge semantics with AI needs. Understand researcher data needs to design and build the gold data product, with documentation that evolves, to meet AI applied research needs for a highly efficient AI-first foundation for model R&D.
Curate datasets across modalities. For various AI uses such as generative AI, RAG, predictive and other technique, support researcher needs for chunked and tagged unstructured content with rich metadata, point-in-time-correct features and clean labels. For classical ML and statistical work, deliver model-ready tables.
Build pipelines for reuse. Develop transformations from silver into gold inside Databricks/Spark as scheduled, observable workloads. Design them so researchers can iterate on new features and data mixes without rebuilding from scratch.
Automate quality, filtering, and synthesis. Support research needs for programmatic labeling, weak supervision, near-duplicate detection, boilerplate and noise removal, and LLM-API-driven synthetic data generation where ground truth is scarce.
Version and hand off. Maintain reproducible dataset snapshots. Define clean lineage and semantic definitions so the downstream team can use and re-use gold datasets in AI R&D.
Required Skills and Experience
5+ years building production data systems, with at least 2 supporting ML or AI workloads.
Track record of learning complex new data domains quickly, through reading source code, interviewing experts, and building durable artifacts others rely on.
Advanced Python, SQL, and PySpark/Databricks for working with large, messy data. Expert SQL specifically: comfortable reading complex stored procedures and reverse-engineering business logic from queries.
Databricks ecosystem depth: Delta Lake, Unity Catalog, Spark/PySpark tuning, MLflow.
AI domain literacy: working understanding of embeddings, tokenization, feature engineering, point-in-time correctness, train/validation/test splits, data drift, and the differences between what classical ML and generative models need from data.
Data wrangling across modalities: transforming unstructured content (text, PDFs, transcripts, logs) and structured tabular data into clean, model-ready forms.
AI-friendly data formats (Parquet, Hugging Face datasets) and storage layout decisions — partitioning, sharding, caching, that keep researcher workflows responsive in Azure, AWS or other working environments.
Data quality, filtering, and synthesis pipelines: support for programmatic labeling and weak supervision (e.g. Snorkel or equivalent), near-duplicate detection (MinHash/LSH), content and quality filters, LLM-API-driven synthetic data generation.
Pipeline orchestration (e.g. a la Airflow, Databricks Workflows, Dagster, or Prefect) and dataset versioning including Unity Catalog and feature-store support.
Experience handling regulated or sensitive data under controlled access (HIPAA or equivalent). Familiarity with general de-identification concepts.
Git-based version control and CI/CD for data and code.
Strong written documentation. Skill in eliciting requirements and tacit knowledge from technical and non-technical experts.
Bachelor’s degree in computer science, data science, engineering, statistics, or related field. Equivalent practical experience considered.
Preferred
Hands-on EHR data experience, ideally in skilled nursing, long-term care, post-acute care, or senior living.
Working knowledge of clinical terminologies (ICD-10, SNOMED CT, LOINC) and data standards (HL7v2, FHIR, CCDA).
dbt for transformation and testing.
Familiarity with training-side ML frameworks (e.g. PyTorch) sufficient to debug data-side bottlenecks; experience supporting LLM or foundation-model training or fine-tuning data pipelines.
Clinical NLP, OCR, document parsing, or ASR / transcript pipeline experience.
Data lineage and catalog tools.
Prior experience embedded inside an AI or ML research team.
Master’s degree in a relevant quantitative or computer science field.
What Success Looks Like
AI researchers can start new projects without spending the opening weeks reconstructing what PointClickCare entities mean or rebuilding the same transformations. The gold datasets they need exist, are versioned, are documented, and accelerate work across EDA, experiments, model development, and evaluation. As coverage expands across data types, modalities, and product surfaces, the function grows with it.
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Skills Required
- 5+ years building production data systems, with at least 2 supporting ML or AI workloads.
- Track record of learning complex data domains via source code, expert interviews, and durable artifacts.
- Advanced Python for large data processing.
- Expert SQL, comfortable reading complex stored procedures and reverse-engineering business logic.
- PySpark and Databricks experience (development and tuning).
- Databricks ecosystem knowledge: Delta Lake, Unity Catalog, Spark tuning, MLflow.
- AI domain literacy (embeddings, tokenization, feature engineering, point-in-time correctness, data drift, train/val/test).
- Data wrangling across modalities (text, PDFs, transcripts, logs) into model-ready forms.
- Experience with AI-friendly data formats and storage/layout decisions (Parquet, Hugging Face datasets) in cloud environments (Azure/AWS).
- Experience building data quality, programmatic labeling, weak supervision, near-duplicate detection, and synthetic data pipelines (e.g., Snorkel, MinHash/LSH, LLM-driven synthesis).
- Pipeline orchestration and workflow tooling (Airflow, Databricks Workflows, Dagster, or Prefect).
- Dataset versioning, lineage, Unity Catalog and feature-store support.
- Experience handling regulated or sensitive data (HIPAA or equivalent) and familiarity with de-identification concepts.
- Git-based version control and CI/CD for data and code.
- Strong written documentation and ability to elicit requirements from technical and non-technical experts.
- Bachelor's degree in CS, data science, engineering, statistics, or related field (or equivalent practical experience).
- Hands-on EHR data experience (skilled nursing, long-term/post-acute care) and clinical terminologies (ICD-10, SNOMED CT, LOINC) and standards (HL7v2, FHIR, CCDA).
- dbt for transformation and testing.
- Familiarity with training-side ML frameworks (e.g., PyTorch) to debug data-side bottlenecks and support model training/fine-tuning.
- Experience with clinical NLP, OCR, document parsing, or ASR/transcript pipelines.
- Experience with data lineage and catalog tools.
- Prior experience embedded inside an AI or ML research team.
- Master's degree in a relevant quantitative or computer science field.
PointClickCare Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about PointClickCare and has not been reviewed or approved by PointClickCare.
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Healthcare Strength — Health and dental coverage appear robust, with wellness and assistance programs reinforcing core medical benefits. Coverage quality stands out relative to other benefit elements.
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Leave & Time Off Breadth — PTO and paid holidays are characterized as generous, and flexible work-from-home options are widely available. Occasional extras like summer half‑day Fridays further expand time-off flexibility.
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Flexible Benefits — A customizable mix is evident through remote/hybrid arrangements, day-one eligibility, and a lifestyle or personal spending account. Benefits such as wellness credits and support resources can be tailored to individual needs.
PointClickCare Insights
What We Do
PointClickCare is the market leader driving the transformation of healthcare vulnerable and complex populations through a broad, connected care network powered by deep insights with a commitment to value, outcomes and innovation. We connect post-acute and acute care settings, people and systems like no other company. Our steadfast commitment to our culture and to providing growth opportunities to our employees is evidenced by recent recognition of PointClickCare as one of Canada’s best-managed companies and most admired corporate cultures.
