Top Tech Jobs & Startup Jobs in Vancouver

Optimize and integrate LLMs across the stack from GPU kernels to Kubernetes deployments. Improve inference performance via kernel development, algorithmic techniques (quantization, speculative decoding), and contributions to open-source LLM engines like vLLM. Drive hardware utilization, profiling, and enterprise-grade scalable implementations.

Build pipeline and run full sales cycles for an early-stage AI infrastructure startup: prospecting, technical demos, POCs, negotiation, onboarding, and shaping GTM playbooks while partnering with founders and engineering.

Design, build, and secure scalable distributed backend systems for AI workloads. Implement microservice-based architectures, manage Kubernetes-based deployments, apply IaC and DevOps practices, and collaborate with performance and solutions engineering to optimize model deployment and infrastructure cost, security, and scalability.

Build and own fullstack features across UI, backend APIs, and orchestration interfaces for a global GPU inference platform. Implement real-time streaming, analytics, deployment workflows, developer SDKs/CLI, and observability integrations while collaborating with founders and infrastructure teams to turn distributed systems into user-friendly products.

Build and maintain AI infrastructure for seamless deployment across distributed GPUs, optimizing cost, performance, and flexibility to empower scalable, vendor-agnostic cloud AI workloads.

Rapidly prototype and iterate AI-powered features (LLMs, embeddings, real-time audio, APIs), lead experiments from hypothesis to proof-of-concept, collaborate with product and engineering to translate prototypes into product-aligned solutions, and share learnings and artifacts to accelerate team innovation.