Systems Research Engineer, GPU Programming
Together AI · San Francisco
CHECKED 2H AGO
Infrastructureonsite$200k–$300k
At a glance
Highlights
- Develop and optimize GPU-accelerated kernels for ML/AI applications.
- Co-design GPU kernels and model architecture to boost AI performance.
- Help shape efficient GPU architectures with hardware and software teams.
What they offer
- Startup equity
- Health insurance
Ideal candidate
A systems-minded researcher who loves low-level optimization and co-design, thriving at the intersection of hardware and AI models to push performance frontiers.
Requirements
- 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
- Bachelor's, Master's, or Ph.D. in Computer Science, Electrical Engineering or equivalent experience
- remote policy
- Flexible remote work
Lab profile
About Together AI
AI-native cloud for training and inference on large-scale GPU infrastructure for AI apps and agents.
What they build
- AI-native cloud platform
- GPU clusters for training and inference
- Platform for AI applications and agents
Known for
- Operating large-scale GPU fleets
- Automated infrastructure at massive scale
- Multi-site data center deployment
Key facts
- Over 1M developers
- Operates one of the world's largest GPU fleets
- Infrastructure managing tens of thousands of GPUs