Connectionism: Research Blog by Thinking Machines Lab. Backed by a16z, Accel and GV.
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.
What they're looking for
- 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:
More about this role
The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it.
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.
Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to...
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