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Bachelor/Master/Thesis Position in Global Static Data Reordering with LLVM - Job Opportunity at Intel Corporation

Munich, Germany
Full-time
Entry-level
Posted: January 26, 2025
Hybrid
EUR 45,000 - 55,000 per year for thesis position, with potential for higher compensation upon conversion to full-time role

Benefits

Competitive total compensation package
Stock options and bonuses
Comprehensive health coverage
Retirement benefits
Vacation time
Industry-leading benefits package aligned with tech sector standards

Key Responsibilities

Conduct advanced research on static data layout optimization techniques
Develop and extend LLVM compiler passes for performance optimization
Analyze performance impact using CPU events and benchmarks
Collaborate with senior compiler engineers on implementation
Document findings for academic submission and potential publication
Contribute to LLVM framework enhancement

Requirements

Education

Currently enrolled in Bachelor's or Master's program in Computer Science, Software Engineering, or related field

Required Skills

LLVM framework experience Compiler optimization knowledge Data layout strategies understanding Strong analytical and problem-solving abilities Written and verbal communication skills
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Sauge AI Market Intelligence

Industry Trends

Compiler optimization is becoming increasingly critical as hardware architectures evolve and diversify Growing demand for performance optimization specialists in high-performance computing sectors Increased focus on data-driven optimization techniques in modern compiler design Rising importance of LLVM expertise in both academic and industry settings

Role Significance

Likely part of a specialized compiler optimization team of 5-8 engineers within Intel's larger development organization
Entry-level research position with significant potential for specialized expertise development

Key Projects

Performance optimization for Intel's next-generation processors LLVM infrastructure enhancement for improved data layout Research-driven compiler optimization implementations CPU performance event analysis and optimization

Success Factors

Deep understanding of computer architecture and its relationship to compiler optimization Ability to bridge theoretical concepts with practical implementation Strong research methodology and analytical approach Effective collaboration with senior technical staff Balance between academic rigor and practical engineering solutions

Market Demand

High demand, particularly in semiconductor and hardware-software co-design sectors, with specialized compiler optimization skills being increasingly valued in the industry

Important Skills

Critical Skills

LLVM Framework expertise is essential for direct contribution to optimization efforts Compiler theory understanding necessary for effective optimization implementation Performance analysis capabilities crucial for data-driven optimization

Beneficial Skills

Knowledge of modern CPU architectures Experience with performance profiling tools Understanding of machine learning applications in compiler optimization Familiarity with academic research methodologies

Unique Aspects

Rare opportunity to work on compiler optimization at a hardware manufacturer
Direct application of research to real-world processor performance
Combination of academic research with industry implementation
Access to advanced hardware and performance analysis tools

Career Growth

1-2 years in thesis role with potential for direct hire, followed by 2-3 years specialist development

Potential Next Roles

Compiler Development Engineer Performance Optimization Specialist Research Engineer in Hardware-Software Co-design Technical Lead in Compiler Development

Company Overview

Intel Corporation

Intel leads the semiconductor industry with significant investment in compiler technology and optimization research

Global leader in processor technology with strong research and development focus
Munich office serves as a key European R&D hub with strong academic connections
Research-oriented environment with emphasis on innovation and academic collaboration
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