2 months of paid growth on a technical newsletter: 7 months flat, then 15 to 115 paid in 4 months. What changed wasn't effort.

Quick shape of the graph since I can't attach images: launched paid in June 2025, crawled to about 15 paid subs by October, then a dead flat line through November, December and most of January. From late January to now it went from ~15 to ~115. Same person, same publishing schedule, very different slope.

Newsletter is about production machine learning (I'm an ML engineer at a big tech company, the newsletter is my side thing).

Honest notes on what actually happened:

Months 1 to 5: 0 to ~15 paid. Friends, true believers, people who would have paid for anything I wrote. Zero signal about whether the business worked.

Months 5 to 8: completely flat. Publishing consistently, nothing moved, even lost a few. This is where I almost concluded paid wasn't viable for technical content. The actual problem: I was writing the content I enjoyed (deep technical breakdowns) and paywalling it. People will read technical deep dives for free all day. They pay for something else.

Month 8: changed what goes behind the paywall. Free content stayed technical, that's what builds trust and grows the list. Paid became career strategy: comp, promotion dynamics, what actually gets engineers promoted, career advice and ML job board.

Stuff people can't get from documentation.

Months 8 to 12: 15 to ~115 paid. Same publishing effort. Different paywall logic.

What I'd tell anyone staring at their own flat line:

  1. The plateau usually isn't a volume problem. Publishing more of the thing that isn't converting just gets you a longer flat line.
  2. Track revenue per post by category. When I finally did this, one content type was converting at roughly 4x the other. The data was there for months, I just wasn't looking at it.
  3. Free and paid don't have to be the same content. Free is your top of funnel and credibility. Paid is the thing people can't Google.
  4. ~13k free subs converts to ~115 paid for me, so under 1%. If your free list is small, the paid plateau might just be math, not strategy.

Not selling anything, link in DM only if someone asks.

Happy to answer questions about the numbers.

reddit.com
u/Gaussianperson — 21 hours ago

I started my ML journey in 2019 and have been working as an MLE at a FAANG in Europe since 2022 (mostly recommendation systems, ads, and anti-abuse. Production ML at scale, not research).

Recently in this subreddit I've been seeing a lot of questions about the current job market, breaking in, what the role actually looks like day-to-day, and how to grow once you're in.

I've been answering them individually but figured it'd be more useful to aggregate everything in one thread.

Feel free to ask me about:

  • The 2026 ML job market and how the role is shifting (foundation model engineers vs. AI engineers vs. traditional MLEs)
  • Breaking into ML in 2026 — what I'd actually do if I were starting today
  • How to grow from L3 → L4 → L5 at big tech
  • Making your work visible to leadership
  • Negotiating offers as an MLE
  • What a real day-to-day looks like inside a FAANG ML team
  • Europe-specific stuff (Zürich/London/Berlin comp, taxes, relocation, work culture vs. US)
  • Anything else you think might be relevant for an ML career

I write a newsletter called ML@Scale where I've covered most of these topics in long form. If a question maps to something I've already written 2-3k words on, I'll link the article instead of retyping, but happy to go deep on anything specific in the comments.

Some of the more relevant pieces for this sub:

Ask away!

u/Gaussianperson — 1 month ago
▲ 43 r/MLjobs

I work as an MLE at a FAANG and write about production ML for a living, and the pattern I keep seeing in 2026 is this: the job is splitting into two ends of a barbell.

On one end: foundation model / infra engineers. Deep systems work, JAX/XLA, distributed training, kernel-level stuff. Comp is going up.

On the other end: AI engineers. Shipping LLM-powered products fast, eval harnesses, RAG, agent loops. Also doing well.

In the middle: the "traditional senior MLE": train a model, ship it, monitor it.

This is where the squeeze is happening. Not because the work isn't valuable, but because the differentiation is gone. Every bootcamp grad can do the 80% version.

What this means practically if you're 2-5 years in:

  • Pick a side of the barbell. Don't try to be well-rounded across both — the market doesn't pay for that anymore.
  • If you go infra: get deep on one stack (JAX internals, Triton kernels, distributed training). Shallow knowledge of five frameworks is worth less than deep knowledge of one.
  • If you go AI eng: get good at evals and product sense. The bar isn't "can you call an API," it's "can you ship something that works in production and know when it's broken."
  • Visibility matters way more than people admit. The best MLE I know got promoted because his manager could articulate his impact in one sentence. The work was great, but the framing is what closed it.

Caveat: if you're at a place where the middle still pays well (big tech, finance), this transition is slow. You have time. But the slope is real.

I've written longer on most of this if useful. Happy to share specific links in the comments based on what you're working on, or here's the full set:

u/Gaussianperson — 1 month ago
▲ 2 r/zurich

Hey all! Just wrote an essay on why Zurich is the best city to be in for a young STEM european graduate.

Check it out and lmk what you think! :)

u/Gaussianperson — 1 month ago
▲ 101 r/Substack

Just crossed 100 paid subs on my substack. Sharing what worked for me to give back to the community.

Niche for context: ML engineering / AI infra, technical audience, B2B-ish.

Two and a half years in. 12k free subs, 42k on LinkedIn, and now 100+ paid.

I write about ML engineering (I'm at Google), and almost every "how I grew my Substack" post I read is either vague or survivorship-biased. What worked for me:

  1. Two content lanes, not one.

I run a technical lane (deep dives on ML systems) and a career-strategy lane (what senior MLEs actually do, comp data, interview patterns). The technical lane builds authority and volume. The career lane drives ~70% of revenue at roughly 4x revenue per post. Most people only run one lane and wonder why growth and revenue don't correlate.

  1. Personal/reflective posts convert badly. I use them for top funnel. Speaking of which:

  2. LinkedIn is the top of the funnel, not Substack Notes.

At least for a B2B-ish niche. Six posts a week, each grounded in a specific real thing I did or saw. No generic takes.

  1. A five-email conversion sequence can absolutely fail.

Mine did. It was retrospective ("here's what we covered") instead of forward-looking ("here's what you're about to miss").

  1. Grandfathering existing subs at the old price when you raise prices is the move.

Zero churn from the raise, and the email announcing it converted more free readers than any sequence I've run. I have actually reduced churn!

  1. Content schedule

I x6 on LinkedIn, x3 a week on the newsletter. 2 of them paid only. Lots of different things: ML Job board hand curated, ML deep dives, Personal stories. Everything is tied together!

What's next for me:

Keep grinding. Keep expanding. I will additionally post once per month the "status of the business".

Feel free to ask questions!!

reddit.com
u/Gaussianperson — 2 months ago