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NVIDIA

Data Processing Developer Intern

NVIDIA

. Research and develop techniques to GPU accelerate workloads in deep learning, machine learning, or other AI domains .

Posted 10/14/2026internshipBristol • United KingdomEntry LevelWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in GPU acceleration techniques for deep learning and machine learning, with a strong foundation in parallel programming and performance optimization. Engages effectively with the developer community and contributes to the advancement of next-generation hardware and software architectures.

Highest-signal resume keywords
PhD Or Master Degree In Computer ScienceProgramming Fluency In C/C++Parallel Programming (CUDA, OpenACC, OpenMP, MPI)Expertise In Performance Optimization Of Deep Learning ModelsExcellent Understanding Of Linear Algebra

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills
GPU Acceleration TechniquesDeep LearningMachine LearningAlgorithmsSoftware DevelopmentParallel ProgrammingPerformance OptimizationNatural Language ProcessingComputer VisionRecommender Systems
Soft Skills
Effective CommunicationOrganization SkillsLogical Problem SolvingTime ManagementPrioritization Skills
Industry Keywords
AI DomainsHPC AlgorithmsNext-Generation Hardware ArchitecturesDeveloper Community EngagementOptimization Techniques

Tech Stack

Tools & technologies
C++

About the role

Key responsibilities & impact
  • Research and develop techniques to GPU accelerate workloads in deep learning, machine learning, or other AI domains
  • Work with technical experts from industry and academia to analyze and optimize complex AI and HPC algorithms
  • Optimize AI solutions on modern CPU and GPU architectures
  • Publish and/or present optimization techniques in developer blogs or relevant conferences
  • Engage and educate the developer community
  • Influence the design of next-generation hardware architectures, software, and programming models
  • Collaborate with research, hardware, system software, libraries, and tools teams at NVIDIA

Requirements

What you’ll need
  • Currently pursuing a PhD or Master degree in Computer Science, Computer Engineering, or related computationally focused science degree
  • Programming fluency in C/C++ with a deep understanding of algorithms and software development
  • Background in parallel programming, e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, etc.
  • Effective communication and organization skills
  • Logical approach to problem solving
  • Good time management and prioritization skills
  • Expertise in parallelization and performance optimization of Deep Learning models arising from Natural Language Processing, Computer Vision, Recommender Systems, etc. (standout)
  • Excellent understanding of linear algebra (standout)

Benefits

Comp & perks
  • Equal opportunity and non-discrimination commitment