ML Engineer

Tilde Research; San Francisco (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Tilde Research is a moonshot AI lab focused on mechanistic interpretability, new architectures, and pretraining science. As an ML Engineer, you will build and operate the infrastructure enabling cutting-edge research in training and evaluating large models.

You will optimize inference and training throughput, scale distributed pipelines, and collaborate with researchers to translate insights into measurable improvements in model performance and understanding.

Qualifications

• Experience in deep learning or related research areas.
• Proven capability in ML infrastructure and scalable pipelines.
• Experience with large-scale pre/post-training infrastructure.

Responsibilities

• Build and operate the infrastructure for training and evaluating large models.
• Optimize inference and training throughput; build high-performance distributed training systems.
• Collaborate with researchers to translate insights into measurable improvements in model performance and interpretability.

Skills

Deep learning

ML infrastructure

Open source

Blogging / docs

Pre/post-training infra

PyTorch

JAX

Communication

End-to-end ML pipelines

Tools

Triton

TileLang

TK

Job description

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