GPU Systems Research Intern: Accelerate AI Performance

Together AI; San Francisco (CA); On-site; USD 80,000 - 87,000; Part time

Benefits offered by this job

Housing stipends

Job summary

Together AI in San Francisco hosts an on-site Systems Research Engineer Intern role focusing on GPU-accelerated kernels and ML/AI model optimization. You'll work with modeling, algorithm, hardware, and software teams to co-design architectures and programming models.

The internship runs January to April, 12–14 weeks, with housing stipends and competitive compensation; hourly rate is $58–$63, determined by location, level and role.

Qualifications

• Strong background in GPU programming and parallel computing (CUDA and/or Triton).
• Knowledge of ML/AI applications and models.
• Familiarity with performance profiling and optimization tools for GPU programming.
• Excellent problem-solving and analytical skills.

Responsibilities

• Optimize and fine-tune GPU code to improve performance and scalability.
• Collaborate with cross-functional teams to integrate GPU-accelerated solutions into existing software systems.
• Stay up-to-date with the latest GPU programming techniques and technologies.

Skills

GPU programming

Parallel computing

ML/AI applications knowledge

Problem-solving

Tools

CUDA

Triton

Profiling tools

Job description

Together AI in San Francisco hosts an on-site Systems Research Engineer Intern role focusing on GPU-accelerated kernels and ML/AI model optimization. You'll work with modeling, algorithm, hardware, and software teams to co-design architectures and programming models.

The internship runs January to April, 12–14 weeks, with housing stipends and competitive compensation; hourly rate is $58–$63, determined by location, level and role.