Job Details:
Job Description:
Join an Intel's Datacenter and AI Software Pathfinding team to advance compiler infrastructure for heterogeneous AI workloads. This internship focuses on developing novel optimization techniques for AI kernel compilation targeting both CPU (Intel AMX/AVX-512) and GPU architectures from a unified representation interfacing with MLIR/LLVM framework.
Project Context
Modern AI systems are increasingly heterogeneous: CPUs handle small models, tool execution, feature engineering, and orchestration logic, while GPUs focus on large matrix operations and attention mechanisms of larger models. However, existing compiler frameworks struggle to generate optimized code for both architectures from the same high-level representation (e.g. Helion/Triton DSL).
This internship addresses this challenge by integrating hierarchical optimization abstractions with equality saturation techniques into an MLIR-based compilation pipeline. The goal is to enable automatic discovery and autotuning of high-performance fused kernels through exhaustive algebraic exploration combined with target-specific scheduling decisions.
What You'll Work On
You will explore the design and implementation of a PEG (Graph + PEG) abstraction that combines:
- Algebraic optimization: E-graph-based equality saturation to systematically explore equivalent operator compositions
- Hierarchical scheduling: Multi-level schedule representations (loop tiling, vectorization, memory placement) focus on Xeon CPU and extend to Intel GPU target.
- Cost-driven and constraint pruning: Resource-aware models and constraint satisfiability evaluation to eliminate infeasible schedules early
- MLIR integration: Leverage MLIR's retargetable backend infrastructure for multi-target code generation
- Verification: Prototype equivalence checking using probabilistic and symbolic methods
Why This Internship?
- Cutting-edge applied research: Work at the intersection of research and product in a pathfinding team
- Real-world impact: Contribute to Intel's compiler infrastructure for heterogeneous AI systems
- Mentorship: Learn from experts in compilers, MLIR, and performance optimization
- Publication opportunities: Potential for conference/workshop publications and open-source contributions
- Intel ecosystem: Gain deep knowledge of Intel CPU features (AMX, AVX-512) and GPU architectures
Qualifications:
Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.
Required Qualifications
- Experience with compiler internals or programming languages (IR design, optimization passes)
- Programming skills: Python and C++ (MLIR/LLVM ecosystem desirable)
- Architecture knowledge: Familiarity with CPU (cache hierarchies, SIMD/vector instructions)
- Academic standing: current student of bachelor, master or PhD studies in Computer Science, Electrical Engineering, or related field
Preferred Qualifications:
- Theoretical foundation: Understanding of algebraic rewrite systems and/or e-graphs
- Prior work with LLVM ecosystem, MLIR dialects or equality saturation frameworks (egg, eqsat)
- Experience with autotuning or cost modeling for performance optimization
- Knowledge of probabilistic algorithms and SMT solvers (Z3)
- Familiarity with tensor compiler frameworks: Mirage, Halide, TVM, Triton, or similar
- Publications or projects in compilers, or program synthesis
- Experience with workload optimization for Intel architectures (AMX, AVX-512, Sycl)
Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.
What We Offer:
At Intel, you come to work in a collaborative, supportive environment, where your equally brilliant colleagues will push you to be your best. There's no fear of failure-we know that's how innovation happens. And you'll never be bored.
We offer competitive benefits and pay, opportunities for professional development and the flexibility you need to achieve balance. Intel fosters a collaborative environment allowing the brightest minds in the world to come together to achieve exceptional results.
Besides regular duties you can:
Take advantage of various career development activities.
Participate in various innovation-focused activities (innovation lab, collaboration events, and patent submissions writing).
Have a chance to participate in Intel Great Place to Work program which gathers people who love running, cycling, squash, tennis, cross fit, photography, and many more.
Base salary is accompanied with such additional benefits as bonuses, private medical plan, life insurance, lunch coupons, and more.
Job Type:
Student / Intern
Shift:
Shift 1 (Poland)
Primary Location:
Poland, Gdansk
Additional Locations:
Business group:
The Software Team drives customer value by enabling differentiated experiences through leadership AI technologies and foundational software stacks, products, and services. The group is responsible for developing the holistic strategy for client and data center software in collaboration with OSVs, ISVs, developers, partners and OEMs. The group delivers specialized NPU IP to enable the AI PC and GPU IP to support all of Intel's market segments. The group also has HW and SW engineering experts responsible for delivering IP, SOCs, runtimes, and platforms to support the CPU and GPU/accelerator roadmap, inclusive of integrated and discrete graphics.
Posting Statement:
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Position of Trust
N/A
Work Model for this Role
This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.
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