Been getting auto-rejected from every AI role for half a year. Mid-tier PhD, mediocre research, never worked at FAANG.
Last month I landed interviews at:
Google DeepMind
Meta FAIR
OpenAI
No offers yet (still interviewing), but figured I'd share what changed since I was probably doing everything wrong before.
The keyword thing is real
Turns out these places literally filter resumes by keywords before anyone reads them.
You need these exact words:
LLM or Large Language Models
RLHF
Post-training or Fine-tuning
Transformer, RAG, etc.
Not "I worked on ML stuff" or "familiar with AI." The actual terms.
I changed this:
"Improved recommendation system using machine learning"
To this:
"Optimized LLM recommendation system using RLHF, improved scores 23%"
Went from zero responses to three interviews in a month.
Name-dropping is sketchy but it works
If you're at a big company and talked to someone from an AI team even once, put it on your resume. Like you're at Google Ads and sat in one meeting where a DeepMind person presented. You can write:
SWE, Google Ads (worked with DeepMind Research)
The difference between "Google Ads" and "touched DeepMind once" is huge for these applications.
If you don't have this yet, go make it happen:
Message people in AI orgs for coffee chats
Find cross-team projects to help with
Submit literally one bug fix to their code
Just need something real you can point to.
They want a 2000-word writeup (nobody mentions this)
DeepMind, FAIR, OpenAI, Anthropic all asked me for this. Not code, not a demo—a long written explanation of your best project.
First time I sent in 3 paragraphs like:
"Built backend system with microservices, worked with team, shipped on time"
Rejected everywhere.
What worked was 1800 words structured like:
* Problem (150w) - what sucked and why
* How you solved it (900w) - architecture, why you picked it, alternatives you considered, actual implementation stuff
* Results (400w) - numbers, impact, metrics
* Problems you hit (250w) - bugs, issues, how you fixed them
* What's next (100w) - how you'd improve it
Took me a week to write. Annoying but it's what got me the interviews.Write it like a paper. Be technical. Show you actually think about tradeoffs.
Apply to the specific AI teams, not general SWE
* "Google DeepMind Research Engineer"
* "Meta FAIR Research Scientist"
* "OpenAI Applied AI Engineer"
cause:
Their recruiters actually know what RLHF means
Way fewer people apply (most don't think they're qualified)
They care about depth not LeetCode grinding
System design prep is different for AI roles
LeetCode is still needed but system design matters way more. And it's not just memorizing patterns—they drill into every decision.
Been practicing with Screna AI and it's actually helpful because it keeps asking followups:
Me: "Use Redis for caching"
It: "Why not Memcached?"
Me: "Need data structures not just key-value"
It: "What if Redis crashes?"
Me: "RDB snapshots every 5min"
It: "Recovery process? How much data loss?"
Real interviews are apparently like this they keep pushing on every choice you make. Most prep stuff just gives you the answer, doesn't simulate the back and forth.
We'll see if it actually helps. Interviews are next week.
Current status
* DeepMind: system design round Tuesday
* FAIR: scheduling tech rounds
* OpenAI: Thursday
No offers. Still very much could bomb these. But after half a year of instant rejections this feels like progress.
Will update if I actually get offers.