Research Engineer, Infrastructure, Numerics

Thinking Machines Lab's mission is to empower humanity through advancing collaborative general intelligence. They are seeking an infrastructure research engineer to design and build core systems for efficient large-scale model training, focusing on numerics and distributed training.

Responsibilities:

• Design and optimize distributed training infrastructure for large-scale LLMs, focusing on performance, stability, and reproducibility across multi-GPU and multi-node setups
• Implement and evaluate low-precision numerics (for example, BF16, MXFP8, NVFP4) to improve efficiency without sacrificing model quality
• Develop kernels and communication primitives that use hardware-level support for mixed and low-precision arithmetic
• Collaborate with research teams to co-design model architectures and training recipes that align with emerging numeric formats and stability constraints
• Prototype and benchmark scaling strategies such as data, tensor, and pipeline parallelism that integrate precision-adaptive computation and quantized communication
• Contribute to the design of our internal orchestration and monitoring systems to ensure that thousands of distributed experiments can run efficiently and reproducibly
• Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure