Why Premium

Paid is different. It’s where I go deeper on the things that actually compound: how to read a system and spot what’s broken before it breaks, how to navigate a career in ML without grinding on the wrong things, and every Saturday — The Blind ML Review, where I reconstruct a fictional company’s system design, bury a real failure mode inside it, and walk through the full diagnosis.

Every other Wednesday you also get The Zürich Feed — a curated digest of ML roles in the market, with comp estimates and context on what teams are actually looking for.

Paid isn’t a content archive. It’s a weekly cadence built around the things that separate engineers who compound from engineers who just stay current.

Some of my past premium articles below, enjoy!

🧭 Career Advice

Actionable, specific career strategy from someone actively navigating it at Google. No generic advice — real decisions, real tradeoffs, real experience.

• Gunning for L5 at Google — Here’s what I have done

• My Take On Negotiating Job Offers — Leverage is All You Need?

• How You Actually Grow as an MLE — My honest breakdown of what actually changes at each level, from L3 to L5+

• The Barbell Market for ML Engineers — What four years inside Google taught me about where the profession is heading

• MLE vs SWE vs Research Scientist — The real differences nobody tells you about

• What would I do if I wanted to get into ML in 2026 — If I had to start from scratch today

• Cheat code for MLEs to stand out in 2026 — How to break into MLSys through open source

• The most overloaded role: “Machine Learning Engineer” — aka the new Data Scientist?

• Become a Research Scientist for a day? — Is It Possible?

• Changing Org in Big Tech: your lever for personal growth — the secret trick your current HM does not want you to know ;)

🔬 Technical Deep Dives

5,000+ word production ML systems breakdowns with architecture diagrams and real implementation details. The kind of content that would take you weeks to piece together from papers and blog posts.

• Meta GEM: Bringing LLM Scale Architectures to Ads Recommendation

• Production ML: A reality check on MLOps

• Generative RecSys Won’t Save You: What Actually Matters at Billion-User Scale

• xAI - Recommendation System Deep Dive

• Pinterest Recommendation Systems evolution through the years

• Deep Dive into Claude Code Post Mortem

• Beyond RLHF with Rubrics as Rewards

• Towards Large-Scale Generative Ranking

• Reinforcement Learning with Rubric Anchors

🎙️ About Me

The personal series — what it’s actually like building ML systems at Google, creating content on the side, and thinking about what comes next.

• 2026 is the year of Agency, but not the one you think — Become Agentic!

• A real day in the life of a ML engineer — What actually happens between 9am and 6pm

• Building an Audience While Working Full Time — Why I started this and how I make it work alongside a demanding job

• The Bigger Picture - My life in 10 years? — Where I’m headed and why