Top AI Jobs in Vancouver

Lead development of AI-assisted engineering tooling and infrastructure so agents can autonomously run in cloud dev environments, access CI/CD and internal systems, execute tests, and improve developer productivity through integrations, documentation, and enablement.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

S32 seeks Applied AI Research Scientists to explore new approaches, algorithms, and methodologies to advance AI systems across portfolio companies. Candidates will drive research, shape company direction, and collaborate with founders to apply research outcomes to product and business needs.

Seeking Applied AI Research Scientists to develop and explore new AI approaches, algorithms, and methodologies to drive AI systems across S32 portfolio companies and shape company direction.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

The AI Data Expert will review and evaluate AI-generated outputs and textual content, ensuring cultural relevance and quality. Responsibilities include content analysis, assessing multimedia materials, and providing structured annotations while adhering to detailed guidelines. Native fluency in French and strong English skills are essential, with previous experience in AI evaluation preferred.

Evaluate and review AI outputs, generate or assess content, and ensure cultural relevance and accuracy in various media for AI projects.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

The Applied AI Engineer will develop AI-powered systems to streamline internal processes, collaborating with various departments to enhance efficiency and automation. This role involves problem ownership, system design, and deploying solutions that leverage AI technology while ensuring user adoption and long-term value.

Design and build applications in Python, collaborate with clients, optimize performance, and ensure code quality in AI-driven projects.

Design and implement AI-driven tools to improve workflows across teams. Collaborate to identify internal problems AI can solve, and prototype solutions quickly.

Build and deploy AI/LLM-powered features and RAG pipelines for internet-scale threat intelligence. Prototype, fine-tune, evaluate, and optimize models; integrate AI into platform services; implement CI/CD, regression testing, and security guardrails; collaborate across product, frontend, and data teams to deliver real-time, low-latency AI-driven analytics and automation.

Build, run, and improve AI-driven automations for FP&A, tax, and finance operations. Connect ERP, spend, equity, HRIS, and CRM systems to LLM-powered agents, maintain auditable workflows, and expand the finance team's AI capabilities while supporting forecasting, budgeting, reporting, and month-end close.

Lead the architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines, train LoRAs, integrate control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, deploy internal tools, and partner with design to own the technical roadmap and enable creative teams.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Own client operating rhythm for Creative Operations and Systems/Process Design clients: maintain stakeholder context, turn vague requests into clear actions, use AI-enabled workflows for prep and follow-up, produce client-ready summaries, maintain decision logs and trackers, and coordinate with internal teams to prevent scope drift and improve account knowledge reuse.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

The Senior Applied AI Engineer will build AI-driven interactive product demos, collaborating with design and engineering teams to create intelligent multimodal experiences.

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Lead design and implementation of LLM-powered product features, integrate AI into Vanta systems, instrument evaluations and monitoring, collaborate with platform and product teams, make pragmatic technical tradeoffs, mentor engineers, and drive AI product quality and adoption to help customers accelerate compliance and risk management.