Intermediate Software Engineer

GeologicAI is a Calgary-based start-up developing and deploying exciting new technologies for the energy and mining sectors. We build nifty geological robots that scan rocks, train AI to analyze the scan data, and make fancy software that makes all our results incredibly useful for finding and extracting natural resources. We are well-funded and growing rapidly and looking for amazing people to join our team.

We're a fast-growing well-funded company working on interesting products that are making difference to the world. Our products and services are helping find the hydrocarbons that power our world today, and the metals and minerals required for the energy transition of tomorrow.

The Platform Infrastructure team owns the infrastructure, tooling, and engineering practices that let us ship software safely and scale our systems reliably. This role sits at the intersection of software engineering and Dev Ops: you will build the CI/CD pipelines and deployment automation that get code to production, and you will go deep into the platform itself, profiling performance, identifying architectural bottlenecks, and refactoring the systems that limit our ability to scale.

This is not a role for someone who wants to maintain the status quo. We have the foundations, but we need someone with the instincts to find what is slowing us down next whether that is a fragile deployment pipeline, an unscalable data processing pattern, or a legacy component that needs to be modernized and the engineering depth to fix it

• Design, build, and own automated deployment pipelines from development through test, staging, and production, including deployments to field-based hardware at remote mine sites.
• Implement infrastructure as code (IaC) and configuration management practices that make environments reproducible, auditable, and easy to reason about.
• Drive environment parity across local development, test, staging, and production so that deployment failures are caught early and consistently.
• Automate release processes to reduce manual steps, eliminate deployment risk, and improve release cadence.
• Maintain and improve deployment reliability to distributed and edge
• environments, including trailer-mounted compute units operating in the field.

• Profile and analyze distributed processing systems to identify performance bottlenecks across compute, I/O, memory, and network layers.
• Design and implement optimizations that allow our data pipelines and processing workflows to handle growing data volumes without degrading performance
• Develop pattern detection tooling to proactively surface systemic issues, resource contention, throughput degradation, failure modes before they become incidents.
• Contribute architectural thinking to discussions about how to scale components that were not originally designed for current data volumes

• Work within and improve legacy codebases, identifying the highestleverage refactoring opportunities and executing them without disrupting production systems.
• Containerize Python-based scientific processing applications to improve portability, isolation, and deployment consistency.
• Reduce technical debt incrementally and pragmatically, with a bias toward changes that improve scalability and maintainability simultaneously.
• Establish and promote platform engineering standards around code structure, dependency management, and deployment readiness.

• Build and maintain test environments that accurately represent production at scale, including large-volume data processing scenarios.
• Develop automated regression, integration, and end-to-end validation…