Senior Software Engineer – Data & ML Platform

About GoMaterials

About the role

Production systems

• Own and improve Azure backend services, including serverless services, batch workloads, and ML inference endpoints.
• Manage deployments and reliability, including CI/CD, monitoring, alerting, incident response, and runbooks.
• Operate and optimize cloud compute, autoscaling, container images, identity, and resources.
• Improve production system reliability, scalability, and maintainability.

Data and cloud platform

• Build ETL/ELT pipelines from relational and document databases into analysis-ready datasets and develop a lakehouse-style analytical layer.
• Build and maintain infrastructure as code using tools such as Terraform or Bicep.
• Manage cloud storage, application hosting, identity and access management, Key Vault, and cost optimization.
• Implement workflow monitoring, logging, data-quality checks, and freshness alerts.
• Protect data using access controls, secrets management, and responsible handling of sensitive information.
• Build internal services, APIs, and developer tooling as needs evolve.

ML and engineering practices

• Build infrastructure and tooling for ML and OR experimentation, deployment, evaluation, and reproducibility.
• Turn research prototypes into reliable production services and workflows; own model packaging, versioning, deployment, and CI processes.
• Build automated evaluation and benchmarking pipelines for model performance, drift, and system reliability.
• Establish code quality, automated testing, version control, and CI/CD practices; conduct code reviews and help teammates adopt scalable practices.
• Collaborate with Data Science, Operations Research, Product, and Engineering on integration, technical design, and pragmatic solutions.

Qualifications

• 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.