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Research Engineer, Infrastructure, Numerics
Research Engineer, Infrastructure, Numerics
Thinking Machines Lab is seeking a Research Engineer, Infrastructure, Numerics to design and build core systems for efficient large-scale model training with a focus on numerics. This role involves improving the numerical foundations of the distributed training stack, including precision formats, kernel optimizations, and communication frameworks. The ideal candidate will thrive at the intersection of research and systems engineering, contributing to stable, scalable, and fast training of trillion-parameter models.
About the role
About the Role
We’re looking for an infrastructure research engineer to design and build the core systems that enable efficient large-scale model training with a focus on numerics. You will focus on improving the numerical foundations of our distributed training stack, from precision formats and kernel optimizations to communication frameworks that make training trillion-parameter models stable, scalable, and fast.
This role is ideal for someone who thrives at the intersection of research and systems engineering: a builder who understands both the math of optimization and the realities of distributed compute.
What You’ll Do
- 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.
Skills and Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
- Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
- Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
- A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
- Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases in areas such as floating-point numerics, low-precision arithmetic, and distributed systems.
Preferred qualifications — we encourage you to apply if you meet some but not all of these:
- Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM.
- Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs.
- Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA.
- Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models.
- Experience training and supporting large-scale AI models.
- Track record of improving research productivity through infrastructure design or process improvements.