Top AI Jobs in Vancouver

The AI/ML Engineering Manager leads ML engineering teams, manages client engagements, shapes architecture, ensures technical quality, and drives business growth through ML operations.

The Senior Software Engineer, Voice AI will design and implement complex systems for emergency response communications, oversee AI integrations, and provide technical mentorship.

Lead design and deployment of production-grade AI solutions, build multi-agent systems and cloud-native ML infrastructure, apply rigorous statistical evaluation, implement generative and predictive models, champion MLOps and data pipeline best practices, and collaborate with clients to translate business needs into technical solutions.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Lead AI technical strategy, architect AI systems, set safety standards, mentor engineers, and evaluate AI models at Midi Health, a healthcare-focused company.

You will design and build LLM-powered features, integrate AI capabilities into workflows, and contribute to internal practices at a healthcare-focused company.

As an AI/ML Architect, you will define architecture for AI solutions, guide teams, mentor, and align initiatives with business goals, focusing on ethical practices.

As a Staff AI Software Engineer, you will design and build autonomous AI systems for security testing, focusing on reliability, scaling, and integration with enterprise infrastructure, all while collaborating with top security experts.

Create high-quality video content and integrate AI tools into production workflows, combining storytelling and video craft to enhance cinematic quality.

Lead the creative development of AI-assisted video content, overseeing multidisciplinary teams, driving innovation, and integrating Generative AI into video production workflows.

Lead Android-first development of growth experiments and features, using AI-native workflows to design, implement, instrument, and analyze A/B tests. Ship performant, reliable Android experiences at scale, mentor teammates, participate in on-call, and codify AI-native engineering playbooks for broader adoption.

The Senior AI Engineer will design, build, and ship AI-powered product features, integrating LLMs and automating workflows to enhance security insights and user experience.

Build and run scalable AI-first GTM automation: design multi-agent architectures, integrate LLM APIs and marketing/CRM platforms, implement CI/CD and observability, and enable partner teams with templates, docs, and training to drive measurable GTM outcomes.

The Senior Software Engineer will develop AI-driven internal tools for automation across Sales, Customer Success, and Marketing, ensuring scalable workflows and system integrations with various platforms.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business systems (BigQuery, CRMs, Slack), implement RAG pipelines, observability, governance, and scalable serverless/containerized services. Partner with GTM teams to deliver measurable automation and self-service tooling.

Lead AI product initiatives from concept to production, define architecture, collaborate with product leadership, and improve AI workflows.

Design and build multi-agent AI systems and RAG pipelines, orchestrate LLMs and embeddings, develop scalable full-stack (Node.js/Next.js) APIs, optimize infra (SQL, AWS/GCP, Docker/K8s), lead technical design-to-deployment, ensure code quality, and implement video content classification and tagging for influencer marketing.

Lead strategy and architecture for GTM and finance systems (Salesforce, NetSuite, Data360/Snowflake, Agentforce). Manage a Salesforce admin, design and operate autonomous Agentforce agents, ensure data governance and a unified customer profile, oversee API integrations and migrations, and build testing and release processes to maintain system integrity and compliance.

Designs, builds, tests, and deploys AI-enabled automations and copilots for GTM and RevOps. Creates reusable agent templates, prompt libraries, and orchestration patterns; implements end-to-end API integrations; monitors performance; documents and hands off production solutions; and trains/coaches business teams on safe, responsible AI usage and prompting best practices.

Translate cutting-edge ML and physics research into production-grade scientific code, architect and scale large simulation and training pipelines on GPU infrastructure, optimize hardware-level performance, support models through full product lifecycle, and integrate complex datasets and ML models into external-facing drug and materials discovery products.

The role involves developing advanced automation workflows using Claude and Claris MCP for a demonstration and webinar, requiring strong technical skills and independent work.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

The Mid/Senior AI Cinematic Video Editor will create narrative-driven video content using AI tools, manage production workflows, and ensure visual cohesion.

The Senior Staff AI Engineer will lead the design and optimization of reinforcement learning systems for enterprise applications, focusing on scalable architectures and production deployment. Responsibilities include mentoring, collaboration, and ensuring compliance with AI ethics and safety standards.