Jalasoft
via Workable
Applied ML Engineer - Content Developer (Optimization & Foundations)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 10h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 4d ago
The date the source published, not the day we noticed it (2026-09-11). Last seen at its source 3h ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Mexico, Colombia, Argentina, United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $4,100–6,938/mo
Middle 50% of 14 listings that do state pay — Engineering · all levels · Mexico · USD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
workable employer's own board first seen 3d ago · last seen 3h ago
The listing
Jala University is an innovative initiative designed to bridge the gap between academia and industry by delivering practical education tailored to industry needs, with a unique educational model integrating experts from both academia and industry.
Our goal is to transform the economies of underserved regions through the software industry, creating professional opportunities that impact individuals, communities, and regions, while leaving a lasting legacy for future generations
Requirements
- 5+ years shipping production software, including 2+ years in production AI/ML
- Able to implement optimizers from first principles (not by calling a framework's built-in optimizer) and instrument gradient flow, learning-rate schedules, and regularization diagnostics
- Working fluency in probability and information theory (likelihood, entropy, KL divergence)
- Able to personally build a course artifact to production standard, including an instrumented optimizer and analytical brief
- Hands-on with Python, NumPy, scikit-learn, PyTorch (basics through autograd/custom optimizer loops), Jupyter, Docker, one cloud VM (AWS/GCP/Azure), Weights & Biases, Modal (CPU + T4/A10), matplotlib
- Public writing samples showing technical explanation (docs, workshop material, book chapter, or open-source project known for its docs), including ability to write to publication standard
- Reproducibility discipline (pinned dependencies, containers, seeded runs, documented decoding parameters)
- Professional written English
Benefits
- Remote work modality (home office).
- Joining a dynamic and growing organization with international reach.