Applied ML Engineer – Content Developer, Optimization & Foundations

Job not on LinkedIn

🔥 37 minutes ago

🌐 Mexico, Colombia, +2 more countries – Remote

🇲🇽 Mexico – Remote 🇨🇴 Colombia – Remote 🇦🇷 Argentina – Remote 🇺🇸 United States – Remote

⏳ Contract/Temporary

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Jalasoft

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2003

☁️ SaaS

📚 Education

Software Development • SaaS • Education

Jalasoft is a global nearshore software development company with a strong presence across 70 cities in 13 countries. With a team of over 1000 South American-based software engineers, Jalasoft specializes in software development, quality assurance (QA), and DevOps solutions. The company focuses on staff augmentation and dedicated teams tailored to meet client needs, ensuring quality and efficiency in project delivery. Jalasoft places a strong emphasis on security, holding an ISO 27001 certification, and partners with leading technology firms like Palo Alto, NVIDIA, and Cisco to offer reliable network and data center management. In addition, Jalasoft operates Jala University, offering educational programs in technology to foster and recruit top tech talent. The company aims to drive digital transformation by providing agile, culturally aligned nearshore software solutions.

📋 Description

• Deliver practical, industry-aligned education through course artifacts and analytical materials • Build an instrumented optimizer from first principles for production-standard educational content • Analyze and document gradient flow, learning-rate schedules, regularization diagnostics, probability, and information theory concepts • Develop reproducible ML course materials using pinned dependencies, containers, seeded runs, and documented decoding parameters • Create technical explanations and publication-standard writing for docs, workshops, book chapters, or open-source documentation • Use Python-based ML tooling, cloud VMs, experiment tracking, and GPU compute to develop and validate content

🎯 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