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.

Responsibilities

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

Required Qualifications

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 experimentationStrong 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 : $150,000.00 – $400,000.00 per year

Benefits

Relocation assistance

Work Location : In person

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