ML Engineer

UniversalAGI

5d•Onsite

About The Position

UniversalAGI is hiring an ML Engineer to help ship ML outcomes by owning the execution layer: data preprocessing/generation, training/fine-tuning, benchmarking, and delivering results.

Requirements

Strong software engineering skills (clean code, debugging, reliability, reproducibility).
Solid ML foundations and hands-on experience with the ML lifecycle: data → training/fine-tuning → evaluation/benchmarking.
Prior experience training or fine-tuning models (any modality/type - LLMs, computer vision, physics, surrogate models, etc.)
Olympic athlete mindset: You have high standards for yourself and are obsessed with measurable improvement on the metrics you are delivering.
Resourcefulness: you know when to do the “quick & correct” fix vs. when to invest in a robust solution, and you can justify the tradeoff with impact/
Ownership: Comfortable owning work end-to-end and being accountable for measurable outcomes.

Nice To Haves

Experience building data pre-processing pipelines for training ML models.
Experience with benchmarking methodology, experiment design, and metric selection.
Familiarity with distributed training / scalable compute workflows.
Experience in an FDE-style / delivery execution role (or similar “ship results fast” environments).

Responsibilities

Build and maintain data preprocessing and data generation pipelines to support model training and evaluation.
Run training and fine-tuning workflows end-to-end and iterate quickly on performance improvements.
Design and execute benchmarking/evaluation suites to measure progress and customer outcomes.
Collaborate with PhD expert researchers to operationalize model architectures into repeatable, production-grade workflows.
Communicate results clearly (metrics, dashboards, short writeups) and maintain high-quality, reproducible work.