Top Tech Jobs & Startup Jobs in Montreal

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.

Optimize and scale search applications, implement performance monitoring, develop benchmarking frameworks, and ensure high availability of search services.

The Head of Sales Enablement will design and execute a sales enablement strategy, developing resources, metrics, and a high-performing team to drive revenue impact in AI and ML environments.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

This role involves building and scaling compliance programs, translating security and regulatory requirements into scalable solutions, and collaborating across teams, specifically in AI governance and technical compliance automation.

Lead customer success for AI solutions in Montreal, building relationships with stakeholders and ensuring successful AI adoption and value realization while navigating complex regulatory requirements.

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 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.

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.