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ML Engineer (Internship and Full-time)

Open 65d

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

What this application asks

ashby

Name, Email, Resume

  • Are you interested in internship, full-time, or both?
  • Personal Website/Blog optional
  • GitHub optional
  • Twitter optional
  • LinkedIn optional
  • Describe a technically challenging bug you encountered while building ML training or inference infrastructure. What made it hard to debug, and how did you ultimately resolve it? written answer
  • Which deep learning frameworks and tools have you worked with (e.g., PyTorch, TensorFlow, JAX, Triton, CUDA)? How many years of experience with each? written answer
  • What are your career goals and why do you think this role helps actualize them? written answer
  • Is there anything you'd like us to know about you? written answer · optional

See also

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