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Performance Analyst Intern information

What does a performance analyst intern do?

A Performance Analyst Intern typically assists with collecting, analyzing, and interpreting data to evaluate the effectiveness and efficiency of business processes or operations. They help prepare reports, identify trends, and provide actionable insights to support decision-making. Interns often work with various teams, use analytical tools, and contribute to improving overall organizational performance while gaining hands-on experience in data analysis.

What is the difference between Performance Analyst Intern vs Data Analyst Intern?

AspectPerformance Analyst InternData Analyst Intern
Required CredentialsRelated coursework in performance metrics, analytics, or businessRelated coursework in statistics, data analysis, or computer science
Work EnvironmentTypically in finance, marketing, or operations teams focusing on performance metricsOften in IT, finance, or consulting teams analyzing large datasets
Employer & Industry UsageUsed in industries emphasizing performance optimizationCommon across industries focusing on data-driven decision making

The Performance Analyst Intern focuses on evaluating and improving performance metrics within a company, often working with operational or marketing data. In contrast, the Data Analyst Intern handles broader data analysis tasks, including data cleaning, visualization, and reporting. Both roles require analytical skills and familiarity with data tools, but their focus areas and industry applications differ slightly.

What are the key skills and qualifications needed to thrive as a performance analyst intern, and why are they important?

To thrive as a Performance Analyst Intern, you need strong analytical skills, proficiency in data interpretation, and a background in business, finance, or a related field. Familiarity with data analysis tools such as Excel, Tableau, or SQL, and experience with performance tracking systems are typically required. Attention to detail, effective communication, and a proactive approach to problem-solving distinguish top candidates. These skills are crucial for accurately analyzing performance metrics, generating actionable insights, and supporting decision-making within the organization.

What types of projects or analyses do performance analyst interns typically work on during their internship?

Performance Analyst Interns are often involved in projects that require collecting, analyzing, and interpreting data related to business operations or financial performance. Typical tasks include building reports, identifying performance trends, and supporting the team in developing data-driven recommendations for process improvements. Interns frequently collaborate with departments such as finance, operations, and IT to gather relevant data and present their findings to stakeholders. This hands-on experience helps interns develop analytical skills and gain a comprehensive understanding of how different teams contribute to organizational success.
What are the most commonly searched types of Performance Analyst jobs in California? The most popular types of Performance Analyst jobs in California are:
What are popular job titles related to Performance Analyst Intern jobs in California? For Performance Analyst Intern jobs in California, the most frequently searched job titles are:
What job categories do people searching Performance Analyst Intern jobs in California look for? The top searched job categories for Performance Analyst Intern jobs in California are:
What cities in California are hiring for Performance Analyst Intern jobs? Cities in California with the most Performance Analyst Intern job openings:
Performance Analyst Intern jobs near you
Infographic showing various Performance Analyst Intern job openings in California as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 2% Temporary, and 2% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

Inference Optimization Intern - Performance Modeling

Institute of Foundation Models

Sunnyvale, CA • On-site

Internship

Re-posted 18 days ago

Job description

About the Institute of Foundation Models
The Institute of Foundation Models is dedicated to advancing the science and engineering of large-scale AI systems. Our researchers and engineers develop cutting-edge foundation models while pushing the limits of high-performance computing and efficient AI inference. By combining deep expertise in machine learning, systems engineering, and hardware optimization, we build scalable AI solutions that drive scientific discovery and real-world impact.
As part of the team, interns work alongside world-class researchers and performance engineers to optimize the execution of large-scale foundation models on next-generation NVIDIA GPU architectures. This internship provides hands-on experience in low-level GPU performance analysis, kernel optimization, and hardware-aware inference acceleration.
Key Responsibilities
This intensive internship offers a unique opportunity to contribute to the development of a simulator and profiling framework for foundation model inference on NVidia GPUs.
Responsibilities include:
  • Develop analytical performance models for GPU kernels and inference workloads.
  • Build and validate a simulator to estimate theoretical hardware performance limits.
  • Compare measured kernel performance against architectural peak throughput.
  • Identify performance bottlenecks in compute, memory, communication, and scheduling.
  • Analyze GPU execution using NVIDIA Nsight Systems and Nsight Compute.
  • Investigate PTX and SASS code generation to understand low-level execution behavior.
  • Collaborate with researchers and engineers to optimize inference kernels for transformer-based models.
  • Evaluate utilization of Tensor Cores, memory bandwidth, caches, and instruction pipelines.
  • Design profiling methodologies for Hopper and Blackwell architectures.
  • Document findings and provide actionable recommendations for performance improvements.

Academic Qualifications
Currently pursuing a degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, High-Performance Computing, or a related quantitative discipline.
Preferred Qualifications
  • Experience with CUDA programming and GPU kernel development.
  • Understanding of NVIDIA GPU architecture and memory hierarchy.
  • Familiarity with performance profiling tools such as Nsight Systems and Nsight Compute.
  • Knowledge of PTX, SASS, and low-level GPU execution.
  • Experience optimizing CUDA kernels for throughput and latency.
  • Understanding of roofline analysis, performance modeling, and hardware utilization metrics.
  • Experience with deep learning frameworks such as PyTorch or TensorFlow.
  • Strong programming skills in C++, CUDA, and Python.

Desired Skills
  • Performance engineering mindset.
  • Strong analytical and debugging abilities.
  • Interest in AI systems, inference optimization, and hardware-software co-design.
  • Ability to work independently on research and engineering challenges.
  • Excellent written and verbal communication skills.

Apply