Thinking Machines Lab

Thinking Machines is seeking an engineer to design, build, and operate the GPU supercomputing environment essential for large-scale AI model training and inference. The goal is to deliver high-performance, reliable, and cost-efficient compute resources, enabling users and researchers to accelerate their work at scale. This is an evergreen role, and while immediate openings may not always align perfectly, applications are continuously reviewed for future opportunities.

$350k - $475k

Thinking Machines is seeking an infrastructure research engineer to design, optimize, and maintain the compute foundations essential for large-scale language model training. This role involves developing high-performance ML kernels, enabling efficient low-precision arithmetic, and enhancing the distributed compute stack. You will work closely with researchers and systems architects, bridging algorithmic design with hardware efficiency. The position is ideal for someone who enjoys hands-on work with hardware and across research boundaries, prototyping new kernel implementations, profiling performance, and defining scaling strategies for next-generation AI systems.

$350k - $475k

Thinking Machines is seeking an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. The goal is to make experimentation and training fast and reliable, allowing research teams to focus on science rather than system bottlenecks. This role is ideal for individuals with deep systems and performance expertise combined with a curiosity for machine learning at scale, who will own the training stack end-to-end to ensure every GPU cycle drives scientific progress.

$350k - $475k