Systems Research Engineer Intern - GPU Programming (Winter 2027)
About this role
Together AI builds an AI Native Cloud platform that offers high-performance inference, fine-tuning, and large-scale pre-training for artificial intelligence applications. Systems Research Engineer Interns focus on developing and optimizing GPU-accelerated kernels and algorithms to improve the performance and efficiency of machine learning systems.
Working alongside modeling, algorithm, hardware, and software teams, interns co-design specialized GPU kernels and architecture. This position suits individuals with strong analytical abilities who want to research advanced hardware programming techniques and scale infrastructure during a 12 to 14 week term.
What you will do
- Develop, optimize, and fine-tune GPU code to maximize system performance and scalability.
- Partner with cross-functional groups to integrate accelerated solutions into current software platforms.
- Research emerging developments and methodologies in GPU programming to maintain high infrastructure standards.
- Co-design efficient GPU architectures and programming models alongside hardware and software engineers.
What you need
- Demonstrate a strong background in parallel computing and GPU programming, including experience with CUDA or Triton.
- Bring working knowledge of machine learning and artificial intelligence applications and models.
- Understand performance profiling and optimization tools used in GPU development.
- Possess strong analytical and problem-solving skills for complex systems engineering.
Benefits
- Competitive compensation
- Housing stipends
Skills and tech stack
Questions candidates ask
Where is this internship located?
The internship is based on-site at the company headquarters in San Francisco.
What are the dates for the Winter term?
The internship program runs from January 4th to April 9th, covering a duration of 12 to 14 weeks.
What is the pay rate for this role?
The estimated US hourly rate for this position ranges from $58 to $70 an hour, determined by experience, skills, and job-related knowledge.