Edmonton, Alberta

Job Summary The Developer Success Solutions (DSS) team provides development support to software engineering community by managing, maintaining, and troubleshooting internal software applications and tooling. This role is critical in ensuring engineers have the resources and assistance needed to deliver high-quality products efficiently.

Key Responsibilities

Support &

• Troubleshooting Respond to and triage support questions and requests from engineers.Extract and analyze information from log files and other sources to debug issues, identify owners, and determine root causes.Manage and escalate complex support queries, leveraging advanced data tools for in-depth analysis to resolve issues quickly, especially those impacting ML model development cycles. Tooling &

- Automation Develop basic tools, scripts, and test cases to improve the speed and quality of support.Create debugging scripts and documentation to automate and accelerate support responses.Provide actionable feedback to engineering teams to enhance product reliability and performance. Documentation &
- Collaboration Collaborate with technical writers to improve documentation in areas covered by support.Contribute to product development by providing insights and suggestions for the product roadmap. Reporting &
- Insights Produce regular reports for leadership,



summarizing program performance and support metrics.Offer data-driven insights and recommendations to inform product development and strategic decisions.

Skill Requirements

Programming Experience 3-8 years of hands-on experience in software development using at least two of the following languages: C++, Java, or Python, including development of small to medium-sized applications. Front-End Technologies Experience with at least one front-end web technology (e.g., React, Angular, Vue.js) is preferred. Systems & Databases 2-3 years of experience working with databases and Linux environments. Application/Dev Support Prior experience in application or development support is a plus.

Machine

Learning & AI Basic understanding of machine learning models and common algorithm types.1–2 years of hands-on experience in at least one area of ML model lifecycle (design, implementation, deployment, training, or testing).Familiarity with high-level building blocks of AI implementation.

Other Requirements

1. Optional But Valuable Certifications: Certified Validation Engineer (Cve) Or Equivalent.

1. Optional But Valuable Certifications In Project Management (E.G., Pmp, Agile).

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