FY27 Intern - ML Compiler Research Intern - 6 months, Strasbourg, France

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Company:

Qualcomm France S.A.R.L.

## Job Area:

Interns Group, Interns Group > Interim Intern

General Summary:

Opportunity Overview:

Join our Compiler Labs team in Strasbourg, France, for an internship focused on compiler and runtime techniques for efficient Generative AI inference on modern AI accelerators.

Generative AI models increasingly require sophisticated optimization to achieve high performance within the compute, memory, and power constraints of edge devices. Traditional ML compilation approaches typically

decompose a model into individual operators and optimize or schedule them separately. An alternative approach is to optimize larger portions of the model jointly, enabling more coordinated decisions about computation, data

movement, memory usage, and hardware resources.

During this internship, you will investigate compiler and performance-optimization techniques for these larger-scale, highly optimized execution models. Depending on your background and the direction of the project,

the work may involve automatic exploration of optimization strategies, compiler-driven code generation, intermediate representations, memory planning, or performance analysis.

Representative Projects:

• Explore automatic optimization and auto-tuning techniques for high-performance AI kernels.
• Investigate compiler techniques for generating and optimizing specialized implementations from model or intermediate representations.
• Develop profiling-guided approaches for evaluating and selecting alternative implementation strategies.
• Explore efficient management and reuse of on-chip memory using compiler analysis of tensor lifetimes and data dependencies.
• Analyze the interaction between computation scheduling, data movement, memory usage, and model structure.
• Evaluate new optimization techniques on representative Generative AI workloads and accelerator architectures.
• Develop experimental compiler or performance-analysis infrastructure to guide future optimization work.

The exact research direction will be selected according to the candidate's background and interests, together with the technical opportunities available at the beginning of the internship.

Toolchain & Environment

The internship may involve technologies such as:

• C++ and Python
• LLVM and MLIR
• PyTorch and related model representations
• AI accelerator programming and toolchains
• Performance profiling and benchmarking tools
• Compiler optimization and code-generation infrastructure

Candidate Profile

We are looking for a student in Computer Science, Computer Engineering, Electrical Engineering, or a related field with a strong interest in some combination of:

• Compilers and program optimization
• Machine learning systems
• High-performance computing
• Parallel programming
• Computer architecture
• Generative AI and Large Language Model inference

Experience with C++, Python, LLVM/MLIR, GPU or accelerator programming, or ML frameworks is valuable, but experience in all of these areas is not required.

The ideal candidate is curious, comfortable investigating open-ended technical problems, and interested in understanding the interaction between machine-learning workloads, compiler transformations, memory systems, and hardware execution.

What You Will Gain

• Hands-on experience with compiler and performance-engineering problems for modern Generative AI workloads.
• Exposure to intermediate representations, code generation, accelerator execution, and quantitative performance analysis.
• The opportunity to build and evaluate a focused research prototype within an experienced compiler team.
• Experience translating experimental results into practical engineering recommendations.

*References to a particular number of years experience are for indicative purposes only. Applications from candidates with equivalent experience will be considered, provided that the candidate can demonstrate an ability to fulfill the principal duties of the role and possesses the required competencies.

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