Join MLE Path, the podcast that explores what it truly means to grow and thrive as a machine learning engineer.
Hosted by an industry veteran with over 14 years of experience—including leadership roles at Adobe, Twitter, and Meta—this podcast brings you candid conversations with senior ML engineers at the forefront of the field. Together, we uncover the skills, strategies, and insights that define excellence in machine learning engineering at every career stage.
From navigating technical challenges to mastering leadership and innovation, MLE Path provides actionable advice and thought-provoking discussions to help you elevate your career, whether you’re a rising ML engineer or a seasoned professional.
This is your guide to continuous growth and impact in the ever-evolving world of machine learning.
Valerii Babushkin - Mastering ML Careers & System Design
In this episode, Valerii Babushkin, Senior Director at BP and former Meta E7, shares insights on career transitions, ML system design, and navigating high-level IC and management roles. He discusses why he moved between IC and leadership positions, the challenges of onboarding at top tech companies, and how to identify what truly matters in a company. Valerii also dives into engineering communication, the importance of documentation, and why a strong writing culture is key for success. We also explore ML system design principles, including how to write an effective design doc, choosing the right loss functions and metrics, and avoiding common pitfalls like data leakage. Valerii shares his thoughts on Kaggle, when it’s useful for career growth, and what differentiates staff, senior staff, and principal engineers. If you’re aiming to level up in ML, this episode is packed with actionable career and technical advice! 🚀 🔗 Resources Mentioned 📖 Valerii’s Book: Machine Learning System Design https://amzn.to/4hGaX5l 🔗 Connect with Valerii on LinkedIn: https://www.linkedin.com/in/venheads 📄 Staff Engineer Book: https://amzn.to/4hCfq9l
11 Feb 2025
Ville Tuulos - The Art of Machine Learning Timing, Tools, and User Empathy
In this episode, we sit down with Ville Tuulos, CEO of Outer Bounds and creator of Metaflow, to discuss why machine learning is so hard—and how to make it easier. Ville shares his journey from training neural networks at 13 to leading ML infrastructure at Netflix, where he built tools to empower data scientists. We dive into ML cycles, user empathy, infrastructure, and what separates great ML engineers from the rest. 📚 Learn More About Ville Tuulos & Metaflow - Ville Tuulos on LinkedIn: https://www.linkedin.com/in/villetuulos/ - Outer Bounds: https://outerbounds.com - Effective Data Science Infrastructure: How to Make Data Scientists Productive (Ville’s book): https://amzn.to/4hN0Yee (affiliate link)
3 Feb 2025
Kevin Van Horn - Following Jaynes and Why Fundamentals Matter
Dr. Kevin S. Van Horn joins the podcast to discuss Bayesian probability, ML optimization, and lessons from his career. He shares insights on NP-hard problems in ML, Bayesian methods, and how to ensure success in ML projects.
Resources & Links:
Kevin’s Newsletter: https://epistemicprobability.substack.com/ (https://epistemicprobability.substack.com/)
Jaynes' Book on Bayesian Probability: https://amzn.to/42GFOKu (https://amzn.to/42GFOKu)
26 Jan 2025
Rebecca Bilbro - Open source, entrepreneurship, and 14 years in ML
Dr. Rebecca Bilbro shares her unique journey through data science and machine learning. We explore the value of blending unusual skill sets, fostering collaboration, and maintaining control in her career.
Rebecca also reflects on her transition from an individual contributor to an entrepreneur, the importance of building strong teams, and the transformative role of open source in the industry.
She offers insights into what makes a great machine learning engineer, the challenges of entrepreneurship, and the critical role of contributor energy in open source projects. We also discuss optimizing machine learning workflows, effective communication in managing ML projects, navigating the hype around ML technologies, and fostering community engagement and transparency in the field.
Links:
LinkedIN (https://www.linkedin.com/in/rebeccabilbro/)
Rotational (https://rotational.io/)
Applied Text Analysis with Python (https://amzn.to/4h8O2iR) (affiliate link)
Reach and audience
Public platform figures. Ratings count people who left a rating, not total listeners.
YouTube views
4,909
Score snapshot 15 Sept 2025
Apple Podcasts (US)
5.0 / 5
2 ratings
Podcast Authority Score: 43 / 100
A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.
Quality
44
Social presence
0
YouTube
74
Engagement
32
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