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 scalable, efficient training of large models for deployment and research. Your goal is to make experimentation and training at Thinking Machines fast and reliable to ensure our research teams can focus on science, not system bottlenecks.
What they're looking for
- Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar
- Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
- 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
- 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 scalable, efficient training of large models for deployment and research. Your goal is to make experimentation and training at Thinking Machines fast and reliable to ensure our research teams can focus on science, not system bottlenecks.
This role is ideal for someone who blends deep systems and performance expertise with a curiosity for machine learning at scale. You’ll take ownership of the training stack end to end, ensuring every GPU cycle drives scientific progress.
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 apply. We continuously...
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