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Design and build modular, high-performance distributed systems for AI model inference. Lead architecture decisions involving concurrency, data movement, APIs, system boundaries, and reliability. Develop extensible interfaces and composable components for model developers and contributors. Diagnose complex performance bottlenecks and support production software. The role values systems programming, abstraction design, GPU and accelerator optimization, inference frameworks, and research awareness.

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 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.

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.

Design, build, and maintain developer tooling for the MAX AI inference stack, including profilers, tracers, debuggers, dashboards, and agent-native tools. Integrate tooling across MAX, Mojo, and driver APIs, collaborate with kernel/model/serving engineers, and drive performance analysis, debugging, and observability to improve developer productivity and production reliability.