AI Compiler Engineer
AI Compiler Engineers build compilers and optimization tools that make AI models run faster on various hardware. They work on frameworks like XLA, TVM, and MLIR to optimize AI workloads.
What is a AI Compiler Engineer?
AI Compiler Engineers build compilers and optimization tools that make AI models run faster on various hardware. They work on frameworks like XLA, TVM, and MLIR to optimize AI workloads.
Education Required
Master's or PhD in Computer Science, preferably in compilers or systems
Certifications
- • Compiler courses
- • Systems programming
Job Outlook
Specialized demand at AI chip and framework companies. High compensation.
Key Responsibilities
Develop AI compilers, optimize model execution, support new hardware backends, improve framework performance, collaborate with hardware teams, and contribute to open source.
A Day in the Life
Required Skills
Here are the key skills you'll need to succeed as a AI Compiler Engineer.
Python
Programming in Python for AI/ML development, data analysis, and automation
C++
C++ programming language
LLVM/MLIR
AI compiler frameworks
Compiler Development
Building compilers
Performance Optimization
Optimizing system and model performance
Hardware Architecture
Understanding hardware
Salary Range
Average Annual Salary
$230K
Range: $160K - $300K
Salary by Experience Level
Projected Growth
+45% over the next 10 years
ATS Resume Keywords
Optimize your resume for Applicant Tracking Systems (ATS) with these AI Compiler Engineer-specific keywords.
Must-Have Keywords
EssentialInclude these keywords in your resume - they are expected for AI Compiler Engineer roles.
Strong Keywords
Bonus PointsThese keywords will strengthen your application and help you stand out.
Keywords to Avoid
OverusedThese are overused or vague terms. Replace them with specific achievements and metrics.
💡 Pro Tips for ATS Optimization
- • Use exact keyword matches from job descriptions
- • Include keywords in context, not just lists
- • Quantify achievements (e.g., "Improved X by 30%")
- • Use both acronyms and full terms (e.g., "ML" and "Machine Learning")
How to Become a AI Compiler Engineer
Follow this step-by-step roadmap to launch your career as a AI Compiler Engineer.
Learn Compiler Fundamentals
Study compiler design, optimization passes, and code generation.
Study ML Compilers
Learn TVM, XLA, MLIR, and ML-specific compilation.
Understand ML Workloads
Know neural network operations that need optimization.
Learn Hardware Targets
Understand GPU, TPU, and other hardware compiler targets.
Build Optimization Skills
Develop expertise in graph and loop optimization.
Contribute to ML Compilers
Work on open-source ML compiler projects.
🎉 You're Ready!
With dedication and consistent effort, you'll be prepared to land your first AI Compiler Engineer role.
Portfolio Project Ideas
Build these projects to demonstrate your AI Compiler Engineer skills and stand out to employers.
Implement optimization pass for ML compiler
Add hardware target support to ML framework
Improve performance of ML operator compilation
Build model compilation pipeline
Contribute to open-source ML compiler
🚀 Portfolio Best Practices
- ✓Host your projects on GitHub with clear README documentation
- ✓Include a live demo or video walkthrough when possible
- ✓Explain the problem you solved and your technical decisions
- ✓Show metrics and results (e.g., "95% accuracy", "50% faster")
Common Mistakes to Avoid
Learn from others' mistakes! Avoid these common pitfalls when pursuing a AI Compiler Engineer career.
Optimizing without measuring real workloads
Ignoring numerical precision impacts
Not considering hardware constraints
Over-specializing without generalization
Missing edge cases in transformations
What to Do Instead
- • Focus on measurable outcomes and quantified results
- • Continuously learn and update your skills
- • Build real projects, not just tutorials
- • Network with professionals in the field
- • Seek feedback and iterate on your work
Career Path & Progression
Typical career progression for a AI Compiler Engineer
Junior AI Compiler Engineer
0-2 yearsLearn fundamentals, work under supervision, build foundational skills
AI Compiler Engineer
3-5 yearsWork independently, handle complex projects, mentor junior team members
Senior AI Compiler Engineer
5-10 yearsLead major initiatives, strategic planning, mentor and develop others
Lead/Principal AI Compiler Engineer
10+ yearsSet direction for teams, influence company strategy, industry thought leader
Ready to start your journey?
Take our free assessment to see if this career is right for you
Learning Resources for AI Compiler Engineer
Curated resources to help you build skills and launch your AI Compiler Engineer career.
Free Learning Resources
- •TVM tutorials
- •MLIR documentation
- •Compiler resources
Courses & Certifications
- •Compiler courses
- •ML systems courses
Tools & Software
- •TVM
- •MLIR
- •LLVM
- •XLA
Communities & Events
- •TVM community
- •MLIR community
- •Compiler forums
Job Search Platforms
- •AI chip companies
- •ML framework teams
💡 Learning Strategy
Start with free resources to build fundamentals, then invest in paid courses for structured learning. Join communities early to network and get mentorship. Consistent daily practice beats intensive cramming.
Work Environment
Work Style
Personality Traits
Core Values
Is This Career Right for You?
Take our free 15-minute AI-powered assessment to discover if AI Compiler Engineer matches your skills, interests, and personality.
No credit card required • 15 minutes • Instant results
Find AI Compiler Engineer Jobs
Search real job openings across top platforms
Search on Job Platforms
Top AI Companies Hiring
💡 Tip: Use our Resume Optimizer to tailor your resume for AI Compiler Engineer positions before applying.