Applied ML Engineer - Content Developer (Optimization & Foundations)
MexicoRemoteContract
AI Summary
Applied ML Engineer focused on building production-grade AI/ML systems and developing educational course artifacts, including implementing optimizers from first principles and writing technical documentation.
About this role
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.
Skills
AutogradAWSAzureDockerGCPGradient FlowInformation TheoryJupyterKL DivergenceMatplotlibModalNumPyOptimizationProbabilityPythonPyTorchRegularizationSciKit-LearnWeights & Biases
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