Tilde Research is a moonshot AI lab advancing mechanistic interpretability, new architectures, and pretraining science. We build foundational understanding of models to advance the frontier of intelligence.
About the role:
As a ML Engineer, you’ll build and operate the infrastructure that makes cutting-edge machine learning research possible. At Tilde, we believe meaningful progress in AI requires not just novel ideas, but the ability to rapidly test, scale, and iterate on them—and that demands exceptional engineering.
You’ll work on the systems that support training and evaluating large models, scaling experimental pipelines, and building the infrastructure necessary to actually understand models. Your work will be foundational to our research, making it possible to explore ambitious ideas that push the boundaries of performance, interpretability, and control.
What you might work on:
Optimize inference and training throughput for novel model architectures
Build and maintain high-performance distributed training infrastructure
Collaborate with researchers to translate insights into measurable improvements in model performance and understanding
You're a good fit if you:
Have experience in deep learning or related research areas
Have demonstrated exceptional capability in working on ML infrastructure. This can include:
Strong open source contributions
Thoughtful technical blog posts/work logs
Previous experience working with large-scale pre/post-training infrastructure
Deep familarity with Pytorch or Jax, basic familiarity Triton/Tilelang/TK etc.
Communicate clearly and effectively, both verbally and in writing
Can design and orchestrate end-to-end ML pipelines
Are able to learn quickly