$170 / month
Start free, decide later
Hi! I’m Yong Lik, a Machine Learning Engineer who has spent the past several years building recommendation systems, search ranking pipelines, and ML infrastructure at Amazon, PayPay, Walmart, and Yahoo Japan. I’ve worked across the full stack of applied ML—from recommenders and search analytics to large-scale ETL, data platform design, and Spark-based interactive tooling for scientists and analysts.
I understand the challenges of learning ML and data engineering, breaking into ML roles, and delivering production systems that truly move business metrics. I mentor people on recommender systems, ML architecture, cloud-based ML workflows, and navigating career transitions into ML or data engineering.
If you need support with ML/data interviews, portfolio projects, system design, or planning your ML career path, I’d love to help. I’m also based in Japan, so if you’re interested in exploring IT roles in Japanese tech companies or startups, feel free to connect.
You tell Yong Lik your goals. They see exactly what you want to work on before session one.
You meet within days. Yong Lik reviews your application and books your first session, usually within a day or two.
You leave with a plan. Concrete next steps and a weekly rhythm from your very first week.
Yong Lik Chang
Machine Learning Engineer at Amazon
Enter your email and we'll let you know when Yong Lik has open spots. You'll also get a link to manage your wishlist – no account needed.
Book a free intro call with Yong Lik
Connect with Yong Lik in a quick call (usually 15-20 minutes)
One-off sessions with Yong Lik
One-off sessions are a great option if you're looking for specific advice on a certain topic.
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What's included in the trial?
Every trial is a little different–here's what Yong Lik says about their trial:
We'll meet for a quick call (usually under 30 minutes) where we:
- Break the ice and introduce ourselves
- Discuss your goals and how I can support you
- Coordinate logistics like time zones, meeting frequency, and communication methods
