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, optimize, and scale the systems that power large AI models. Your work will make inference faster, more cost-effective, more reliable, and more reproducible to enable our teams to focus on advancing model capabilities rather than managing bottlenecks.
What they're looking for
- Bachelor’s degree or equivalent experience in computer science, engineering, or similar
- Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures
- Experience with inference serving systems optimized for throughput and latency (e.g., SGLang, vLLM)
- 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
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, optimize, and scale the systems that power large AI models. Your work will make inference faster, more cost-effective, more reliable, and more reproducible to enable our teams to focus on advancing model capabilities rather than managing bottlenecks.
Our focus is on performant and efficient model inference both to power real-world applications and to accelerate research. This role is responsible for the infrastructure that ensures every experiment, evaluation, and deployment runs smoothly at scale.
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 review...
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