Electi Accelerator.
Working 1:1 with a Big Tech engineer — until you land the offer.
An engineer currently at Google or Meta works with you 1:1 — not a generic plan, but a roadmap built around your exact gaps. You pay the bulk only after you land the offer.
A preparation system built around your weak spots.
Your mentor is not a teacher — they work at the company you're targeting. They passed these exact interviews.
Dedicated FAANG Mentor
2+ private 1:1 sessions per month in the Active Phase. Work directly with engineers from Google and Meta.
Custom Engineering Roadmap
No generic tasks. Your mentor builds a plan focused on your specific gaps: Advanced DSA, System Design, or Behavioral leadership.
Performance Infrastructure
Track your velocity, spot pattern gaps, and know exactly when you're interview-ready — no guesswork.
The Deployment Phase
After the 6-month intensive: direct referrals into hiring pipelines, final interview drills, and salary negotiation support.
Real engineers. Real offers.
Google. Meta. Amazon. Bloomberg. Your mentor didn't just study for these interviews — they passed them, and now work there.
Full-stack engineer with a textbook Big Tech arc: rejected by Meta and Yandex in Year 1, he doubled down on algorithms and LeetCode. Within a year: Bloomberg London → Bloomberg NY → Google Kraków → offers from Meta, Google, and Bloomberg — all in London.
“I wasn't an olympiad winner, didn't get straight A's, and wasn't the smartest. But at some point I had a strong desire to become better. I put in a lot of effort and that opened many doors.”
Leads a mobile product team at Google London. Each career move — from Booking.com to Meta to Google — was a deliberate bet on growth. Chose London for its dense tech ecosystem and the calibre of engineers around him.
“Preparation should become a habit. What matters most is the candidate's inner drive to constantly improve.”
Real progress. In their own words.
Messages from students currently in the program.
Before this I could barely solve algorithm problems, my background was mostly ML and SQL. Now I'm getting through every topic we cover. The mock interview part especially. And I manage to combine it with full-time work.
14:02If you already know some DSA it moves fast, which is good. The only hard part is finding time when you work and study simultaneously. But the intensity, that's exactly the point.
15:45Main thing I'm realizing: I need to stop relying on test runs to debug, interviews don't allow that. Mentor caught that gap before I even noticed it. Algorithm stage feels close to handled now.
18:303 months in and I'm already in interview loops at Netflix and Revolut. First time doing a FAANG-style interview. My mentor called the exact problem that came up in the coding round. I just executed what we practiced.
20:15Before this I could barely solve algorithm problems, my background was mostly ML and SQL. Now I'm getting through every topic we cover. The mock interview part especially. And I manage to combine it with full-time work.
14:02If you already know some DSA it moves fast, which is good. The only hard part is finding time when you work and study simultaneously. But the intensity, that's exactly the point.
15:45Main thing I'm realizing: I need to stop relying on test runs to debug, interviews don't allow that. Mentor caught that gap before I even noticed it. Algorithm stage feels close to handled now.
18:303 months in and I'm already in interview loops at Netflix and Revolut. First time doing a FAANG-style interview. My mentor called the exact problem that came up in the coding round. I just executed what we practiced.
20:15Before this I could barely solve algorithm problems, my background was mostly ML and SQL. Now I'm getting through every topic we cover. The mock interview part especially. And I manage to combine it with full-time work.
14:02If you already know some DSA it moves fast, which is good. The only hard part is finding time when you work and study simultaneously. But the intensity, that's exactly the point.
15:45Main thing I'm realizing: I need to stop relying on test runs to debug, interviews don't allow that. Mentor caught that gap before I even noticed it. Algorithm stage feels close to handled now.
18:303 months in and I'm already in interview loops at Netflix and Revolut. First time doing a FAANG-style interview. My mentor called the exact problem that came up in the coding round. I just executed what we practiced.
20:15Before this I could barely solve algorithm problems, my background was mostly ML and SQL. Now I'm getting through every topic we cover. The mock interview part especially. And I manage to combine it with full-time work.
14:02If you already know some DSA it moves fast, which is good. The only hard part is finding time when you work and study simultaneously. But the intensity, that's exactly the point.
15:45Main thing I'm realizing: I need to stop relying on test runs to debug, interviews don't allow that. Mentor caught that gap before I even noticed it. Algorithm stage feels close to handled now.
18:303 months in and I'm already in interview loops at Netflix and Revolut. First time doing a FAANG-style interview. My mentor called the exact problem that came up in the coding round. I just executed what we practiced.
20:15How the year works.
We divide the year into two distinct phases to ensure you aren't just "learning," but "landing."
Active Sprint
- 2+ personal sessions/mo — technical deep dives and mock interviews
- Rigorous DSA pattern mastery (Hard level)
- System Design for distributed systems
- Refinement of behavioral stories (STAR method/Leadership Principles)
Placement & Support
- Strategic Job Application support
- Direct referrals via our mentor and alumni network
- Peer-to-peer mock interview loops
- Professional salary negotiation to maximize your total compensation
Your career, in numbers
See how your preparation pays off in your first year after getting hired.
Using default $10,800/yr (approx $900/mo)
Estimates are based on public market salary data and typical preparation timelines. Your actual result depends on your experience and effort.
Pay less now. More only when it worked.
Monthly fee stays low so the barrier to start is low. We earn the rest when you land.
Accelerator
For engineers who want personal mentorship and faster progress.
+10% Success Fee.
- From 2 one-on-one mentor sessions per month
- Fully personalized preparation plan
- Mock interviews with feedback
- Resume and application strategy
- Coding + system design curriculum
- Progress tracking platform
- Private community access
How Success Fee works
Success Fee means we win only when you win.
You pay a percentage of your first-year salary only after you accept a job offer.
This model ensures our goals are perfectly aligned. We invest in your potential, and we only see a return when you succeed.
Why we use this model
- It lowers your upfront cost
- It aligns our incentives with your result
- Train with top mentors without full price
How it works
You prepare with mentors
You get an offer
Pay only after signing
No payments if
- You do not get hired
- You stop before reaching interviews
You choose how to pay
- Low monthly payment + Success Fee
- Higher monthly payment + 0% Success Fee