This is a fully remote position, open to applicants in Mexico, +3 more countries.

📋 Description

• Provide practical, industry-relevant education through course materials and analytical resources.

• Construct an optimized instrument from foundational principles for high-quality educational content.

• Examine and document gradient flow, learning-rate schedules, regularization diagnostics, probability, and concepts in information theory.

• Create reproducible machine learning course materials utilizing pinned dependencies, containers, seeded runs, and documented decoding parameters.

• Generate technical explanations and publication-quality writing for documentation, workshops, book chapters, or open-source resources.

• Leverage Python-based machine learning tools, cloud virtual machines, experiment tracking, and GPU computing to develop and validate content.

⛳️ Requirements

• Over 5 years of experience delivering production software, with at least 2 years focused on production AI/ML.

• Capable of implementing optimizers from scratch (not merely utilizing a framework's built-in optimizer) and instrumenting gradient flow, learning-rate schedules, and regularization diagnostics.

• Proficient understanding of probability and information theory (including likelihood, entropy, and KL divergence).

• Competent in personally developing a course artifact to production standards, which includes an instrumented optimizer and an analytical brief.

• Experienced with Python, NumPy, scikit-learn, PyTorch (basic to advanced custom optimizer loops), Jupyter, Docker, and one cloud VM (AWS/GCP/Azure), along with Weights & Biases, Modal (CPU + T4/A10), and matplotlib.

• Public writing samples demonstrating technical explanations (such as documentation, workshop materials, book chapters, or renowned open-source projects) showcasing the ability to write to publication standards.

• Adheres to reproducibility practices (pinned dependencies, containers, seeded runs, documented decoding parameters).

• Proficient in professional written English.