Stories

From the vetted research file. Each story has a vetting status. Verified means key claims were confirmed by the person's own primary source or reputable independent sources. Unverified means an anonymous Reddit account with an internally consistent story. Read these for the patterns, not as promises.

Verified, first-hand, and repeatable

Aleksa Gordić, DeepMind Research Engineer (2021) without an ML degree. EE bachelor's, dropped out of a master's. About 1 year of intense software engineering self-study, 1.5 years of slower ML learning, then 1.5 years of very intense ML study, while working full time at Microsoft (2 hours before work, 2 to 3 after). Got the Microsoft job via an ML summer camp after failing a Facebook interview. Finished all of Andrew Ng's courses, then studied one AI subfield per three-month block: read the seminal papers, implemented and open-sourced a project, made YouTube videos, wrote a summary post. Networked at a conference; a DeepMind researcher who knew his work referred him. Prepared for interviews by researching every interviewer's papers and knowing every resume project in depth. Says a Research Scientist role would likely have needed a PhD. In 2025 he added: joining a great small team matters more than a famous lab. Source: https://gordicaleksa.medium.com/how-i-got-a-job-at-deepmind-as-a-research-engineer-without-a-machine-learning-degree-1a45f2a781de

Chris Olah, co-founder of Anthropic, no degree. Left University of Toronto after about a year. Worked independently on ML, took a Thiel Fellowship, wrote widely read explainer posts on neural networks, which led to a Google Brain internship and then a research career. The strongest truly-self-taught example, and the one whose public-writing route is most copyable. Source: 80,000 Hours podcast, https://podcasts.apple.com/us/podcast/108-chris-olah-on-working-at-top-ai-labs-without-an/id1245002988?i=1000531710782

Jeremy Howard, fast.ai founder, philosophy BA. Taught himself ML in industry, became known through Kaggle results, built fast.ai and co-authored ULMFiT. His one-line advice: "train lots of models." Source: https://www.latent.space/p/fastai

Partially verified, first-hand

u/bralynn2222, Director of AI at 21, no degree, while working as a line cook. Started in 2023 fine-tuning early open models. Spent six months hand-building Python reasoning datasets and fine-tuned a 4B coding model (exists on Hugging Face). Releasing it got an unpaid role on an open-source team, his first resume entry. Started consulting for small businesses via ads, LinkedIn and cold email at about $3k a month. A repeat client hired him full time at about $84.5k. Five hours a day after shifts on one RTX 3090. Lessons: data quality beats scale, build many small models, start consulting cheap. Commenters noted the pay is low for the title. Source: https://www.reddit.com/r/LocalLLaMA/comments/1vi8jlr/comment/p2btrbo/

u/aifordevs, Staff ML Engineer at a FAANG company, started ML in 2015 from software engineering. Says the easiest way in is joining an ML team in a regular software role. Spent three months on the Transformer and two weeks building one from scratch. Recommends Zero to Hero, a GPT-2 reproduction, and a free GitHub blog as a portfolio. Got the FAANG role through friends' referrals; networks at panel events after researching the panelists. In 2025 said a master's does help with FAANG hiring and he still feels bias without one. Source: https://www.reddit.com/r/learnmachinelearning/comments/1d1u2aq/comment/l5wl4af/

Unverified but detailed and widely upvoted

u/certain_entropy, senior AI researcher at a startup (81 upvotes). Creative writing undergrad. Taught himself to code building websites for non-profits. IT analyst, then university research analyst (picked up data science over four years), then technical project manager at an AI lab where he taught himself ML. Data scientist at a startup, volunteered to build its deep learning feature, became "the deep learning guy," got recruited to lead DL elsewhere. Lessons: be generous, don't ask permission (download code and tinker), find an unsolved problem at work, focus on problems not tools. Source: https://www.reddit.com/r/MachineLearning/comments/ojp76r/comment/h546lvl/

u/irndk10, data scientist, mechanical engineering BS (2,000+ upvote manifesto). Five years as an analyst first. Told his boss he wanted to move into data science, took small parts in DS projects, got given one, did well, got promoted. MOOCs didn't stick; daily structured coding for 30 to 60 minutes did. Lessons: basic math is enough to start; framing and evaluation matter most; domain knowledge plus data exploration beats raw technical skill. Source: https://www.reddit.com/r/datascience/comments/1hp7pim/

u/Ballsfor11days, data scientist in 20 months (2017). Quant master's. Changed jobs quickly to get more hands-on data work, pay 47k to 65k to 90k to 115k. Used employer training reimbursement for courses. Automated real work tasks to have talking points. About 50 applications, 3 screens, 1 offer. Studied 3 to 4 hours daily. Negotiated every offer. Source: https://www.reddit.com/r/datascience/comments/713hnw/

u/obolli, ETH Zurich ML student at about 29, no schooling past 6th grade. Re-learned math from arithmetic with Khan Academy mastery challenges, then textbooks; proved theorems himself; tested on past exams; passed the entrance exam. The route exists (ETH admits by entrance exam) but this is self-taught math into a degree, not into a job. Source: https://www.reddit.com/r/learnmachinelearning/comments/1nexb3h/

Short ones. u/hollammi: took a web dev job, pitched an ML project on the company's energy data as a money-maker, became "the AI guy." u/nuclearpowered: automated parts of a boring job with simple ML, got noticed, was asked to join the ML team. u/Icko_: bootcamps, junior dev, a high placement in a DrivenData competition got the first data science job. u/xoinq: applied to small companies with fewer applicants. u/Apprehensive_Bug_906: applied ML to a niche computational biology problem, got a co-author publication, leaned on it in every interview.

Recent first-person accounts outside Reddit

Janvi Kalra, AI engineer at OpenAI (2025). As a software engineer at Coda she asked to join the AI team and was turned down. She taught herself LLM app-building through hackathons and side projects, became the team's go-to LLM person within months, and was an easy hire when it expanded. "It's much easier to transfer internally." Interviewed at 46 companies and saw every format. Source: https://newsletter.pragmaticengineer.com/p/from-software-engineer-to-ai-engineer

Nathan Lambert, post-training lead at Ai2. Asked professors to join their groups and was told no twice; credits "consistently asking for opportunities" and releasing work often early on. Wrote his RLHF explainer as a way of learning the topic. Source: https://www.interconnects.ai/p/my-path-into-ai

The patterns, ranked by evidence

Pattern	Evidence	Verdict
Adjacent job, then sideways into ML	9 of the profiled people	Strongest
Implement papers and models from scratch, publish the code	Gordić (verified), aifordevs, certain_entropy	Well supported
Make learning public (blog, YouTube, GitHub, open models)	Olah, Gordić, bralynn2222	Well supported; this is what brought referrals
Referrals from people who know your work	Gordić, aifordevs	Well supported
Depth in one niche or domain	Top answer in the 2026 "what actually worked" thread	Well supported
Solve a real problem at your current job	certain_entropy, hollammi, nuclearpowered, Ballsfor11days	Well supported
Just-enough math, fill gaps as needed	irndk10, aifordevs, Gordić	Holds for engineering, not research
Daily study on top of a job, 1.5 to 3+ years	Gordić, bralynn2222, Ballsfor11days, irndk10	Well supported
Competitions	Icko_, Gordić, Howard	Mixed; an ex-FAANG MLE calls them useless for big-tech MLE roles
Consulting or unpaid work for first experience	bralynn2222	One case; worked, low pay
Safety fellowships without a PhD	MATS exists and says so	Program verified

Survivorship bias, stated plainly

Of the famous "self-taught" names, only Olah, Radford and Hotz truly lack a degree. Shazeer (IMO gold) and Nanda (Cambridge math) are prodigies with elite degrees. Tero Karras was named in a thread with no evidence either way. The repeatable path in this course is the adjacent-job one, not the dropout-to-lab one.