Top Tech Jobs & Startup Jobs in Charlotte, NC

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.