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Lead technical programs for Modular’s platform engineering organization, coordinating build, test, release, and continuous deployment infrastructure across compiler, runtime, kernel, cloud, and product teams. Establish planning and reporting cadences, manage dependencies and risks, define developer-experience metrics, coordinate migrations and rollouts, improve infrastructure reliability, and partner with Security on CI/CD integrity, artifact isolation, signing, provenance, and access control.

Build developer tools for Modular’s MAX AI inference stack, including profiling, tracing, debugging, correctness validation, metrics, dashboards, and agent-native workflows. Integrate tooling across MAX, Mojo, driver APIs, and orchestration layers while supporting heterogeneous hardware. Collaborate with kernel, model, and serving engineers to improve developer productivity, performance analysis, and deployment health.

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.

Leads developer community programs for Mojo and MAX, including Champions, meetups, virtual meetings, hackathons, conferences, social media, documentation, and moderation. Owns program strategy, event logistics, vendors, speakers, budgets, community communications, and scalable systems that enable community-led growth. Requires technical community management, advocacy program development, event operations, technical communication, and strong project execution.

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

Champion the Mojo programming language by creating technical tutorials, blogs, videos, benchmarks, and examples; supporting developers across online communities; presenting at conferences; gathering community feedback for engineering and roadmap decisions; and collaborating with language, compiler, product, and marketing teams to grow adoption.

Developers Relations Engineer focused on MAX inference and serving. Responsibilities include benchmarking MAX against competing platforms, deploying and profiling CPU/GPU inference workloads, creating technical documentation and tutorials, supporting customer deployments, debugging community issues, presenting at technical events, and informing the product roadmap. The role requires hands-on production ML inference experience, familiarity with serving infrastructure and benchmarking methodology, strong Python and systems programming skills, and the ability to explain complex technical concepts clearly.

Develop and mature the LLDB-based Mojo debugger, including interactive debugging, IDE integration, debug information, expression evaluation, GPU debugging, profiling, and MCP capabilities. Define the debugger roadmap, collaborate with compiler and developer relations teams, engage the developer community, and support open-source contributions. The role requires LLDB internals expertise and strong knowledge of debuggers, linkers, compilers, and systems programming.

Lead and grow a team of approximately 12 kernel engineers developing high-performance inference kernels across NVIDIA, AMD, and ASIC hardware. Own the MAX kernel roadmap, performance optimization, portable kernel architecture, and delivery. Partner with framework, serving, compiler/runtime, hardware enablement, and Qualcomm teams while advancing AI-assisted development, verification, and engineering practices.

Own developer-facing product marketing for Mojo and MAX, including release messaging, technical content, launch plans, executive presentations, community engagement, competitive positioning, and adoption analytics. Partner with engineering, product management, and developer advocates to explain AI infrastructure, GPU performance, inference optimization, and programming language capabilities to technical audiences.