Top AI Jobs in Vancouver

Design and build AI-driven interactive prototypes and production-ready flows using AI coding tools. Own prompt-to-pipeline solutions, validate via experiments and user research, produce high-fidelity UX in Figma, and collaborate with engineering to move prototypes to production while aligning business, design, and tech.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

The Co-op Software Engineer will work on AI infrastructure projects, optimizing systems for scalability and performance while collaborating with teams.

The AI Enablement Specialist will lead AI integration into commercial real estate research, focusing on data strategies and process improvements through workshops and collaboration with teams.

Manage the vision, strategy, and roadmap for Contract Intelligence AI products. Collaborate with teams to define requirements and market strategies, ensuring delivery of generative AI solutions.

Design and implement advanced Generative AI models, optimize performance for LLMs, and collaborate with cross-functional teams to innovate AI solutions within the automotive sector.

As a Staff SRE, you will ensure the reliability and performance of Andromeda's GPU infrastructure, lead incident responses, build observability systems, and mentor engineers, while collaborating closely with engineering and customers.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

Own and operate the delivery platform and embed with an engineering team to improve deployment automation, CI/CD, GitOps, IaC, and production reliability. Lead AI-augmented ops (runbooks, alerting, incident response), build environments, set standards, tune streaming and datastore infrastructure, and mentor teams while tracking DORA and reliability metrics.

Develop and maintain MCP tools and the MCP Toolbox for Databases; expand test coverage for agentic coding assistants; refactor and maintain test frameworks; convert technical posts into production-ready sample code and documentation; build real-world data applications (QueryData) to support product R&D and collaborate with cross-functional teams to accelerate agentic AI adoption.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Build and scale a full-stack Next.js application tracking PRs, CI health, test parity, and benchmarks across AI accelerator open-source projects. Integrate GitHub APIs and webhooks, design PR/metrics schemas, visualize results, and produce technical digests and reports on cross-accelerator parity and performance trends.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

Lead frontend design and delivery of AI product experiences using React and TypeScript, mentor engineers, set frontend standards, use AI-assisted tooling, and contribute to backend services in Go and product roadmaps.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

As a Backend/AI Systems Engineer, you will develop backend systems for AI workflows and enhance core functionalities for Spellbook's legal software. You'll tackle complex problems, ensure system reliability, and collaborate with product teams, while maintaining a hands-on ownership approach.

Lead operational rhythms and client delivery, focusing on AI solutions. Collaborate with executives and manage multi-stakeholder projects for positive outcomes.

Perform hands-on product and player analytics across onboarding, retention, monetization, progression and LiveOps. Build and QA ETL pipelines in an AWS analytics stack, write cost- and performance-aware SQL, support A/B testing, and create reusable AI-enabled analytical workflows and notebooks to inform product, marketing, and LiveOps decisions.

Lead strategy and execution for AI-powered customer experience, owning CX AI roadmap, internal AI productivity tools, metrics, and a team. Partner with Product, Engineering, and Data to build, deploy, and iterate AI systems across chat, voice, and email to improve containment, agent productivity, QA automation, and customer outcomes.

Design, build, and deploy production-grade AI-powered backend systems. Integrate LLMs, vector databases, and task-specific ML models into scalable, low-latency architectures. Implement background processing, performance tuning, testing, monitoring, and error reporting. Debug cross-layer issues and collaborate with product and frontend teams to deliver reliable AI-driven features.

Lead the design and delivery of an end-to-end AI system layer across the product: agentic workflows, orchestration, evaluation, guardrails, observability, and developer platform. Build and grow a multi-team org, partner with Product and Design, define how AI decisions flow across cross-product context, and ensure reliable, measurable, and safe autonomous behaviors that proactively help small-business users.

The Principal Business Consultant, AI will lead projects, consult with clients on AI solutions, and improve business processes, ensuring optimal software adoption and utilization.

Evaluate AI-generated coding interactions, assess quality of engineering judgments, provide feedback, and define standards for AI coding agents. Comfortable making subjective judgments about engineering quality.

Lead the development of AI training cluster frameworks and optimization technologies, enhancing efficiency and performance for large model training scenarios.