GoMaterials is one of Canada's fastest-growing companies, recognized by Deloitte, the Globe & Mail, and the Lazaridis Scaleup Program. We’re revolutionizing how landscape contractors source plant and hardscape materials through a B2B marketplace that simplifies procurement in a traditionally outdated industry. Since our inception, we have helped landscapers save time, money, and stress and plant over 1.5 million plants and trees.

We’re looking for a Senior Software Engineer to take ownership of the production systems and internal data platform that power our Machine Learning (ML) and Operations Research (OR) work. This is a hands‑on, high‑ownership role at the intersection of backend engineering, cloud infrastructure, and data engineering . You’ll work closely with our ML and OR specialists to ensure models and optimization solutions can move reliably from experimentation into production.

You’ll inherit existing production systems and have the chance to improve and evolve them over time—from architecture and infrastructure to deployment, observability, and developer tooling. As our needs grow, you’ll also help shape the roadmap for our internal data and ML platform. You’ll be the primary owner of these systems, collaborate directly with technical specialists and product teams, and have significant influence over architecture, tooling, and engineering practices.

Own, operate, and improve backend services running in Azure, including serverless services, batch workloads, and ML inference endpoints. Manage deployments and reliability across environments, including CI/CD, monitoring, alerting, incident response,



and operational runbooks.

Build Our

Data & Cloud Platform Design and build reliable ETL/ELT pipelines that transform data from relational and document databases into analysis‑ready datasets. Help develop our lakehouse‑style analytical layer and the infrastructure that supports it. Implement monitoring, logging, data‑quality checks, and freshness alerting across data workflows.

Ensure data is handled securely through appropriate access controls, secrets management, and responsible treatment of sensitive information. Enable ML & Operations Research Build the infrastructure and tooling our ML and OR specialists need for experimentation, deployment, evaluation, and reproducibility. Own model packaging, versioning, deployment, and CI processes for ML and OR codebases.

Build automated evaluation and benchmarking pipelines to monitor model performance, drift, and system reliability. Partner with Data Scientists and OR specialists to run and operationalize experiments. Establish strong practices around code quality, automated testing, version control, and CI/CD.

Conduct peer code reviews and help teammates adopt scalable engineering practices. Help ensure our codebases remain maintainable and releasable as the team and platform grow.



Work closely with Data Science, Operations Research, Product, and Engineering to integrate ML and optimization solutions into our products.

Contribute to technical design discussions and decisions around architecture, scalability, reliability, and performance. Translate technical and business needs into pragmatic engineering solutions. Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent practical experience.

Strong programming skills in Python and SQL .

Experience building and operating production data pipelines end to end , including concepts such as retries, idempotency, backfills, orchestration, and freshness monitoring.

Experience with cloud infrastructure concepts such as serverless and batch compute, object storage, identity and access management, and monitoring. Strong knowledge of Git, CI/CD, automated testing, and modern software engineering practices. Comfort working with ML or OR codebases and model artifacts—you don't need to be the person building the models, but you should be comfortable reading, running, packaging, and deploying them.

Azure Machine

Learning, MLflow, or DVC. Azure data governance and security practices. DevOps or SRE practices related to observability, reliability, and performance. Optimization, logistics, transportation, or large‑scale ML systems. Grow with us through learning & promotion opportunities Enjoy solid health benefits & time off Get a piece of the pie with equity after your first year

📌 Senior Software Engineer - Data & ML Platform (Mississauga)
🏢 Lever
📍 Mississauga