Staff AI Performance Engineer
Salary not listed • Milpitas, California, United States • Full-time
Posted 22d ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
About the job
Lead performance optimization for large-scale AI workloads, transforming them into faster and more efficient systems. This role involves analyzing and optimizing AI training and inference across distributed systems, connecting compute, memory, and networking behavior. You will lead complex performance workstreams, identify bottlenecks, validate improvements, and translate performance data into actionable engineering decisions. The position requires building benchmarks, profiling demanding workloads, and developing C++ and Python tools for analysis and optimization, ultimately helping teams enhance efficiency, scalability, and reliability. This role offers broad technical scope across AI infrastructure at data center scale.
Responsibilities
- Analyze and optimize AI training and inference workloads across distributed systems.
- Connect compute, memory, and networking behavior to improve system performance.
- Lead complex performance workstreams across hardware and software.
- Identify performance bottlenecks and validate improvements.
- Translate performance evidence into practical engineering decisions.
- Build benchmarks and profile demanding AI workloads.
- Develop C++ and Python tools for performance analysis and optimization.
- Provide recommendations to improve efficiency, scalability, and reliability of AI systems.
Requirements
- Strong experience profiling and optimizing AI, machine learning, or high-performance computing workloads.
- Experience with distributed systems and communication libraries (e.g., MPI, NCCL, UCX, libfabric).
- Strong C++ and Python skills, including experience with performance-sensitive software.
- Deep understanding of compute, memory, and communication behavior in large-scale systems.
- Ability to lead technically complex work and coordinate cross-team improvements.
- Familiarity with MLPerf, accelerated architectures, ML frameworks, or high-performance interconnects.
Benefits
- Competitive compensation and benefits.
- Medical, dental, and vision coverage.
- Mental health, wellness, and employee assistance resources.
- Retirement savings benefits with company contributions.
- Paid vacation, sick time, company holidays, and parental/family leave.
- Life insurance and short-term/long-term disability coverage.
- Flexible working hours.
- Hybrid working arrangements.
- Professional development resources and learning programs.
- Office amenities and team-led activities.