Senior Software Engineer – Data & ML Platform
New
G
GoMaterialsData and ML platform
Listing location: Canada; Workplace type: Remote; Structured job location: CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLGitAzureCI/CDRESTful APIsTerraformDistributed Systems
Requirements
- 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.
- Strong understanding of APIs, backend service design, and distributed systems.
- Experience building and operating production data pipelines end to end, including retries, idempotency, backfills, orchestration, and freshness monitoring.
- Hands-on experience designing and operating production services in Azure or another major cloud platform.
- Experience with cloud infrastructure concepts such as serverless and batch compute, object storage, identity and access management, and monitoring.
- Experience with infrastructure-as-code tools such as Terraform, Bicep, or ARM.
- Strong knowledge of Git, CI/CD, automated testing, and modern software engineering practices.
- Comfort reading, running, packaging, and deploying ML or Operations Research code and model artifacts.
- Experience owning live production systems and improving an existing codebase over time.
Responsibilities
- 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.
- Design and build ETL/ELT pipelines that transform data from relational and document databases into analysis-ready datasets.
- Build and maintain infrastructure-as-code and manage cloud infrastructure, access controls, secrets, and costs.
- Implement monitoring, logging, data-quality checks, and freshness alerting across data workflows.
- Build infrastructure and tooling for ML and Operations Research experimentation, deployment, evaluation, and reproducibility.
- Package, version, and deploy models, and build automated evaluation and benchmarking pipelines.
- Establish practices for code quality, automated testing, version control, and CI/CD; conduct peer code reviews.
- Collaborate with Data Science, Operations Research, Product, and Engineering to integrate ML and optimization solutions.
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