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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.

Design and implement Mojo language features, compiler optimizations, and multi-target compilation for CPUs, GPUs, and AI accelerators using MLIR and LLVM. Collaborate with kernel authors, hardware engineers, researchers, and the open-source community to improve language design, code generation, performance, developer experience, and the broader Mojo SDK.

Own and evolve MAX Framework APIs and developer experience for AI inference and training. Design scalable APIs, programming models, distributed execution primitives, and developer-facing abstractions across Python and Mojo-adjacent surfaces. Lead RFCs and technical specifications, build reference implementations, establish API quality standards, and collaborate with compiler, runtime, kernels, serving, documentation, and DevRel teams.

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 and grow a compiler, kernel, and runtime engineering team responsible for accelerator bring-up across the Modular stack. Drive technical direction, architecture, coding, roadmap delivery, vendor partnerships, SOWs, milestones, cross-team dependencies, hiring, and production-quality model deployment across diverse hardware platforms.

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.

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.

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

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.

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.