Jobs at Modular

Own Modular’s developer-facing community marketing across events, social, and community channels. Manage conference sponsorships, contracts, vendors, booth logistics, and staffing. Plan and publish content on X, LinkedIn, and YouTube, host monthly virtual meetings, recruit speakers, and promote recordings. The role requires strong technical writing, comfort with developer audiences, event execution experience, and travel to conferences, including some weekends.

Develop and mature Modular’s LLDB-based Mojo debugger and broader developer tooling. Responsibilities include interactive debugging, IDE integration, debug information formats, expression evaluation, GPU and agentic debugging, profiling, roadmap definition, and collaboration with developer relations and open-source contributors. The role requires improving developer productivity through programming-language SDK and development-tool contributions, with LLDB and LLVM experience strongly preferred.

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.

Bring up, validate, port, and optimize support for new accelerator architectures across kernels, compilers, runtimes, graph execution, and model serving. Investigate cross-layer correctness and performance issues, develop hardware and ISA expertise, map AI operators to novel platforms, build portability infrastructure, benchmark workloads, and collaborate with hardware vendors to deliver production-quality integrations.

Design and implement Mojo compiler features, optimization passes, and code generation for CPUs, GPUs, and AI accelerators using MLIR and LLVM. Collaborate with kernel authors, hardware engineers, researchers, and open-source contributors to improve compiler performance, language design, developer experience, and the broader Mojo technology stack.

Leads and grows a compiler, kernel, and runtime engineering team responsible for bringing up accelerator architectures across the Modular software stack. Owns technical roadmaps, architecture reviews, code contributions, hardware-platform enablement, vendor SOWs, cross-team dependencies, hiring, and delivery against external milestones. The role requires hands-on expertise in compilers, heterogeneous programming, C++, accelerator programming, and complex software systems, with frequent collaboration with hardware partners and internal engineering teams.

Build and operate a scalable LLM inference platform integrated with the MAX stack. Develop distributed, multi-node model deployments, high-throughput batching, KV-cache management, autoscaling, observability, cold-start optimizations, and multi-cloud capabilities. Create Helm charts and Kubernetes operators while collaborating with kernel and generative AI teams to optimize performance. The role requires backend engineering, Kubernetes operations, machine learning experience, and distributed systems expertise.

Develop and optimize Mojo kernels and implement support for new accelerator architectures across Modular’s AI software stack. Build portability infrastructure, integration tests, and debugging workflows; collaborate with hardware vendors to triage platform issues. Engineers will study ISAs, memory hierarchies, and vendor toolchains while contributing to compiler, model-serving, and inference optimization efforts.

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.

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.

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.