Top Tech Jobs & Startup Jobs in Vancouver

Lead a team building an inference optimization platform for LLMs across GPUs and ASICs. Define technical direction, partner with GTM and engineering to tune customer workloads, create scalable optimization systems, and grow a high-impact performance engineering team.

Profile customer LLM inference workloads end-to-end and apply optimizations across GPU kernels, inference engine, and distributed systems. Build tooling and a repeatable automated optimization platform, partner with GTM and engineering to deploy tuned inference in production, and publish technical insights to shape industry best practices.

The role involves enhancing a graph compiler for ML models, optimizing performance across hardware platforms, and collaborating on core technologies.

Maintain and improve developer workflows and core platform systems: CI/distributed CI, build/release packaging, regression tracking automation, and developer tooling. Support developers across languages and systems, keep CI healthy and responsive, package releases, and build tools to reduce developer pain points while collaborating with engineering and product teams.

Lead a team of backend engineers to build cloud inference services on Kubernetes, focusing on operational excellence, incident response, and engineering best practices.

As a Mojo Libraries Engineer, you'll design and implement libraries for Mojo, focusing on performance and integration with AI systems, collaborating with multiple teams to optimize algorithms and deliver high business impact.

The Backend Engineer will build a multi-cloud inference platform and manage distributed systems for AI model deployment while ensuring operational excellence and low toil services.

The Mojo Compiler Engineer will design and implement features for the Mojo language within an MLIR-based compiler, optimize performance for AI systems, and enhance the developer experience.

As Head of Hardware Partnerships, you will define and scale Modular's hardware ecosystem strategy by managing relationships with hardware vendors, developing partnership models, and supporting partnership economics.

As a GenAI Systems Engineer, you will architect scalable frameworks, optimize distributed systems, and develop APIs, enhancing AI model deployment efficiency.