Machine Learning Engineer

Job Summary

We are a well‑funded early‑stage research team building the engineering foundations required to understand how modern neural networks learn and behave. We are seeking a Machine Learning Engineer to design, scale, and operate the infrastructure behind large‑scale training, experimentation, and mechanistic analysis of transformer‑based models.

• Design, build, and maintain end‑to‑end machine learning pipelines supporting large‑scale training and evaluation of deep neural networks
• Optimize training and inference throughput for transformer‑based and novel model architectures
• Develop and operate distributed training infrastructure across multi‑GPU and multi‑node environments
• Collaborate closely with researchers to translate experimental goals into reliable, scalable engineering systems
• Instrument training runs to surface meaningful signals around optimization behavior, representations, and model performance
• Implement tooling for experiment tracking, reproducibility, and comparative analysis across runs
• Debug training instabilities, performance bottlenecks, and systems‑level failures in complex ML workloads
• Support rapid iteration on architectures, optimizers, and training regimes through robust infrastructure design

• Strong experience in deep learning and large‑scale model training
• Deep familiarity with PyTorch or JAX
  ; working knowledge of lower‑level tooling (e.g., Triton or custom kernels) is a plus
• Proven experience building or operating ML infrastructure for training, evaluation, or experimentation
  
  Strong systems intuition around performance, memory, and distributed execution
• Ability to design and orchestrate end‑to‑end ML workflows, from data ingestion to training and evaluation
• Clear written and verbal communication skills, especially when working with research collaborators
• Ability to learn quickly and operate effectively in an ambiguous, research‑driven environment

Pay: $ – $ per year

• Relocation assistance

Work Location:

In person