GPU Systems Research Intern — CUDA/Triton, On‑Site SF

Togetherai; San Francisco (CA); On-site; USD 80,000 - 96,000; Full time

Benefits offered by this job

Housing stipend

Competitive benefits

Job summary

Together AI in San Francisco HQ is seeking a Systems Research Engineer Intern specialized in GPU programming to help develop and optimize GPU-accelerated kernels and algorithms for ML/AI applications.

This on-site winter-term internship runs January to April, spanning 12 to 14 weeks, with compensation at $58–$70 per hour. Start date may vary by team, with location-based adjustments and housing stipends where applicable.

Qualifications

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

Responsibilities

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

Skills

GPU programming

CUDA

Triton

Parallel computing

ML/AI knowledge

Job description

Together AI in San Francisco HQ is seeking a Systems Research Engineer Intern specialized in GPU programming to help develop and optimize GPU-accelerated kernels and algorithms for ML/AI applications.

This on-site winter-term internship runs January to April, spanning 12 to 14 weeks, with compensation at $58–$70 per hour. Start date may vary by team, with location-based adjustments and housing stipends where applicable.