3 days ago
Waterloo, CanadaMid Level
Responsibilities
- Partner with data scientists to translate research models and prototypes into scalable, production-grade systems.
- Design, develop, and maintain platform components, libraries, and tools that enable data scientists to build, train, and deploy models.
- Architect and develop reliable, performant AI/ML software solutions for production use.
- Integrate and reconcile multimodal healthcare data sources, create derived features, and facilitate data quality assessments.
- Participate in code reviews, mentor junior engineers, and promote software engineering best practices.
- Investigate, troubleshoot, and resolve technical issues affecting the reliability and stability of machine learning systems.
Requirements
- Bachelor’s degree in computer science or a related field.
- At least 2 years of software engineering experience focused on cloud or machine learning systems.
- Proficiency in at least one major programming language, such as Python, Java, or Go.
- Demonstrated delivery of high-impact, production-quality software in a cloud environment.
- Direct experience with MLOps principles and tools for model training and deployment, such as Vertex AI, GKE, and notebooks.
- Preferred familiarity with Terraform and GitHub, and proficiency in Go or Python for distributed systems.
- Preferred production experience with TensorFlow or PyTorch, and familiarity with Spark and Beam.
- Preferred experience with FHIR and databases such as DynamoDB, DocumentDB, or MongoDB.
- Preferred experience with data warehouses and platforms such as Azure Data Lake, Redshift, or Google BigQuery.
Benefits
- Canada base salary of CAD $94,500-$106,000 plus bonus, equity, and benefits.
- Full-time position based in Canada.
- Candidates must be legally authorized to work in Canada; employment-based visa or work permit sponsorship is unavailable for this position.
Tech Stack
Categories
About Verily
Verily builds AI-enabled precision health software and data platforms for health systems, life sciences, and public health organizations. Its products organize multimodal clinical data, support regulated research and clinical studies, and power disease-management and population-health workflows. Founded in 2015, the company sells software and services that make healthcare data AI-ready and interoperable across care, research, and public-health use cases.