Top AI Jobs in Vancouver

The Senior .Net Developer will enhance SDG's C#/.NET Core codebase, design high-load services, and optimize MS SQL queries, benefiting from a remote work environment and attractive benefits.

Design, build, and deploy scalable AI features using LLMs, knowledge graphs, and orchestration frameworks. Integrate models into enterprise products, optimize performance and latency, and manage model lifecycle, observability, and cloud-native deployments. Collaborate cross-functionally, lead engineering workstreams, and maintain high quality, secure production services.

Build and scale production AI systems, APIs, data pipelines, retrieval systems, and model-serving infrastructure for consumer and clinician-facing health products. Integrate and fine-tune LLMs and other models, manage AI feature lifecycles from prototyping through monitoring, and ensure reliability and observability. Collaborate with engineering, product, and science teams. More senior candidates may lead AI architecture, roadmap development, and mentoring.

Own the generation side of Document Intelligence by designing prompts, schemas, validators, multimodal context assembly, and model-selection strategies. Build evaluation datasets and rubrics, monitor factuality and quality, identify regressions, and ship reliable LLM features into production. Work with video, PDF, audio, and image inputs to generate validated maintenance entities while balancing quality, cost, and latency.

Build and deploy AI-powered automations across the business. Partner with teams to identify opportunities, redesign processes, integrate systems through APIs, and launch scalable workflows using LLMs, agents, and automation platforms. Document solutions, establish safeguards, enable adoption, and measure efficiency gains. The role requires strong ownership, responsible AI practices, business understanding, and hands-on experience with AI development, APIs, Python or similar languages, SQL, databases, and modern deployment tools.

Develop backend systems for 3D AutoReview, including geometry parsing, model analysis, quality detection, rule systems, scalable APIs, and AI-assisted review workflows. Collaborate with backend, frontend, product, and machine learning teams to deliver production features. Build extensible systems for custom 3D rules and reference data while experimenting with CAD, machine learning, and large language model technologies.

The role involves optimizing AI training and inference systems, focusing on low-precision training, reinforcement learning, and developing software frameworks for AI accelerators to enhance performance and scalability.

As AI Resilience Fund Manager, you will build and run a fund focused on long-term investments resilient to disruptions caused by AI and economic instability.

Lead development of AI-native production systems: convert AI-assisted prototypes into scalable, secure software; own architecture, reliability, and technical direction; direct and control AI coding agents; define engineering standards and workflows; collaborate with founders on product and strategy; debug complex issues and elevate engineering quality.

Create high-volume, high-quality marketing content using generative AI and AI agents (video, image, social). Develop timely campaign ideas, track AI and cultural trends, experiment with formats, and work closely with founders and the marketing team to shape brand voice and growth.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Develop and deliver AI/ML features (LLMs, RAG, agent orchestration) including backend services in Python, integrate cloud services (AWS Bedrock), design knowledge bases and retrieval, improve prompts and agent reliability, review code, write tests and docs, mentor juniors, and participate in architecture and risk management.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

This role involves optimizing AI systems for large model training and inference, focusing on performance, efficiency, and usability using low-precision training and reinforcement learning techniques.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

Lead architecture and performance exploration for next-generation AI inference accelerators. Build analytical models and simulation frameworks, define subsystem interfaces and roadmaps, and drive hardware/software co-design with compiler, runtime, ML, and silicon teams to optimize latency, throughput, memory, interconnects, power, and cost for evolving LLMs and transformer-based workloads.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Own and automate the end-to-end ML lifecycle: build, deploy, monitor, test, and retire models at scale. Design production-grade, highly available ML infrastructure and CI/CD pipelines, implement governance, and collaborate with engineering and security teams.