Note: The job is a remote job and is open to candidates in USA. NVIDIA is a leading company in computer graphics and AI technology, seeking an AI Compiler Engineer with expertise in compiler technologies. The role focuses on building AI-driven compiler intelligence for production compiler pipelines, delivering improvements in performance and efficiency while collaborating with engineers to shape next-generation compiler capabilities.
Responsibilities
- Join a team driving innovative solutions in compilers and developer tools through applied machine learning and AI
- Collaborate with world-class engineers to shape next-generation compiler capabilities that power large-scale, high-impact products
- Design and implement end-to-end compiler optimization workflows, from feature engineering and model development to compiler integration and production rollout
- Develop and integrate learning-based decision systems into compiler passes, code generation flows, and optimization pipelines
- Build robust interfaces between LLM systems and compiler infrastructure for optimization recommendations, decision support, and workflow automation
- Partner with compiler, architecture, and performance teams to validate impact on representative workloads and benchmark suites
- Lead experimentation, evaluation, and deployment with strong standards for reliability, scalability, and performance
Skills
- BS/MS/PhD in Computer Science or a related field (or equivalent experience), with a focus on machine learning and compiler/developer tools
- 8+ years of software engineering and AI/ML experience, preferably in tools or systems development
- Strong knowledge of compilers, code generation, and GPU architecture
- Demonstrated proficiency in Python and C/C++
- Strong mathematical and scientific foundation relevant to AI/ML and compiler technologies
- Hands-on experience with LLVM/MLIR-based compiler development, including optimization passes, code generation, or frontend integration
- Experience building or integrating LLM-based systems, agents, or developer tooling in production environments
- Deep familiarity with reinforcement learning, genetic/evolutionary algorithms, predictive modeling, and complex systems
- Proven track record of deploying AI/ML solutions in production and embedded environments
- Strong experience integrating LLMs into systems workflows with attention to reliability, latency, and evaluation quality
- Demonstrated measurable outcomes such as runtime gains, compile-time improvements, or efficiency improvements on real-world workloads
- Publications, open-source contributions, or patents in compilers, AI/ML systems, or performance engineering
Benefits
- Equity
- Benefits
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