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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.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

As a Senior AI Kernel Engineer, you will design and optimize high-performance kernels for AI inference on GPUs and custom accelerators, improving performance and collaborating with various teams to influence architecture and implementation.

Drive the product vision for Modular's cloud compute platform, collaborating with teams to enhance AI service capabilities and customer engagement.

The role involves optimizing hardware support within a software stack for AI model deployment, collaborating with teams and hardware vendors to enhance performance and portability across platforms.

Build and evolve Mojo developer tooling including an LLDB-based debugger, IDE integrations, diagnostics, profiling, and GPU debugging. Partner with compiler engineers, shape roadmap, collaborate with DevRel and open-source community, and deliver production-quality debugging and developer workflows.

Lead technical vision and architecture for the Mojo compiler backend: IR lowering, optimization passes, and code generation across CPUs, GPUs, and AI accelerators. Drive MLIR/LLVM initiatives, expand debugging and profiling tooling (LLDB/GDB, LSP, profilers), mentor engineers, collaborate with cross-functional teams, and engage with the open-source community to shape developer tooling and compiler strategy.

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.