Top Tech Jobs & Startup Jobs in Calgary

Evaluate and rank multimodal model outputs, stress-test and document failures, author and edit dataset prompts/responses, build and apply rubrics, perform high-quality annotations across text, image, and structured formats, participate in calibration checks, adapt to evolving tasks, and report model performance trends to cross-functional teams.

Evaluate and label model-generated and human-written code outputs, debug and assess code/agent trajectories, prompt models for complex data science tasks, review visualizations, and report quality trends to improve model behavior.

Evaluate and label model-generated and human-written software engineering outputs for coding, debugging, and codebase-related tasks. Assess agent trajectories, prompt models for complex coding tasks, proofread and improve responses, and report quality and performance trends. Part-time contractor role contributing to model development.

As a Data Annotation Specialist, you will label, audit, and optimize data for AI models in Chinese while ensuring high accuracy and quality. You will perform repetitive tasks and collaborate with teams, providing feedback and recommendations for data improvement.

As a Data Annotation Specialist, you'll label, audit, and correct machine learning data while ensuring high accuracy in Japanese. You'll also evaluate responses and recommend optimizations based on project guidelines.

Provide legal guidance for product development and commercialization: create templates and playbooks, manage IP and open source compliance, mitigate product lifecycle risks, coordinate legal input for launches, advise product, engineering and business teams, and monitor relevant technology regulations.

Drive analytics and measurement for AI product adoption and impact. Build agentic analytics, small language models, predictive models, and customer-facing insights. Design and run experiments (A/B tests, causal inference), ship production models, and translate ambiguous business questions into actionable recommendations used by product, sales, and leadership.

Lead and mentor a GPU infrastructure engineering team to design, deploy, and scale GPU/TPU clusters. Define technical roadmap, implement topology-aware scheduling, monitoring, IaC, and cost/capacity optimization. Collaborate with AI researchers, cloud providers, and cross-functional teams to ensure reliable, secure, and performant infrastructure.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.