Modular Engineering Internship – Summer 2027

Originally posted 2 October 2026 by the employer.

Modular is rebuilding the AI software stack from the ground up.

About the role

This internship involves contributing to AI infrastructure and software technologies spanning the AI Inference stack, with potential focus on projects like AI Inference & Model Serving, GPU & Kernel Engineering, Compilers, Runtimes & Programming Languages, or Distributed & Cloud Systems.

What you'll do

• Optimize AI models for inference workloads.
• Develop high-performance software for GPUs and emerging accelerators.
• Improve compiler and runtime technologies for MAX or Mojo.
• Scale distributed AI systems.
• Build automated performance and quality frameworks.

What you'll need

• Currently enrolled in a bachelor’s, master’s, or Ph.D. degree program in computer science, computer engineering, electrical engineering, software engineering, mathematics, artificial intelligence, or a related technical field.
• Available for 11–14 weeks during Summer 2027 (May–September).
• Expected graduation date of November 2027 or later.
• 1 year of academic or project experience with programming languages such as Python, C, C++, Rust, or Go.

Nice to have

• Experience with machine learning, deep learning, generative AI, LLMs, transformer architectures, AI inference, or ML frameworks and ecosystems such as PyTorch, TensorFlow, or Hugging Face.
• Knowledge or experience with compiler design, compiler/runtime implementation, programming languages, library development, APIs, or frameworks such as MLIR, LLVM, GCC, TVM, or XLA.
• Experience with parallel computing, GPU programming, high-performance computing, CUDA, ROCm, or other accelerator programming models, including kernel optimization, computer architecture, memory systems, memory layouts, or data movement.
• Experience developing or optimizing AI/ML models and workloads, including model acceleration, quantization, profiling, benchmarking, inference optimization, performance analysis, or translating AI research and existing model implementations into production-ready code.
• Knowledge or experience with distributed systems, cloud infrastructure, large-scale AI infrastructure, model serving, Kubernetes, cloud-native technologies, multi-node or multi-cluster deployments, or scalable inference systems.
• Experience with systems programming or software development using Python, C/C++, Rust, Go, object-oriented languages, Python multiprocessing/asyncio, or related technologies.
• Experience with software engineering, application development, operating systems, APIs, software libraries, developer tooling, build systems, release automation, testing, CI/CD, or developer productivity tools.
• Familiarity with AI/ML evaluation, software quality, unit/integration testing, regression detection, performance testing, benchmarking, observability, reliability engineering, reproducibility, or failure analysis.
• Experience with profiling and performance-analysis tools such as NVIDIA Nsight Systems, Nsight Compute, PyTorch Profiler, Linux perf, Intel VTune, or similar technologies.
• Knowledge or interest in software security, AI security, secure development, data protection, access controls, developer security tooling, AI-assisted coding tools, or secure AI systems and workflows.
• Experience building web, full-stack, AI/ML, or systems applications, developer integrations, technical examples, or other developer-facing solutions.
• Experience contributing to open-source projects, GitHub communities, technical documentation/blogs, developer education, or developer-focused technical content.
• Familiarity with AI developer and agentic coding tools or related developer workflows.
• Research experience or publications in relevant AI/ML, systems, compiler, or engineering areas is a plus.

Skills: AI Inference, compiler development, GPU kernels, distributed systems, Mojo programming language