Top Tech Jobs & Startup Jobs in Vancouver

The engineer will develop features for an AI workspace platform, deploy AI solutions in public sector environments, and interact with clients.

Lead the deployment of North in private cloud environments, collaborating with IT teams, designing strategies, and resolving technical issues.

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.

Develop production-grade AI agents for enterprise customers. Design and build LLM-powered systems, integrating workflows, evaluation, and performance standards. Collaborate closely with customers and teams to ensure reliability and effectiveness in real-world applications.

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.

Label, rank, audit, and refine human- and model-generated text to improve LLM safety and policy alignment. Apply nuanced safety guidelines, create adversarial prompts and test cases, identify annotation inconsistencies, and provide feedback. Work independently in a remote, asynchronous team, handling potentially explicit or disturbing content with high attention to detail and consistency.

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.