Mentorship · Foundation and Accelerator

Get into Big Tech with engineers from Google and Meta.

Mentorship in a small group or 1:1: algorithms, system design and getting in front of companies, all the way to the offer.

Sultan Rzagaliyev
Almaz Zinollayev
Timur Umayev

Mentors: Sultan · Meta, Almaz · Ex-Meta, Timur · Google

Mentors from world-class engineering teams
Google
Meta
Amazon
Netflix
Uber
Stripe
Microsoft
Apple
Airbnb
Google
Meta
Amazon
Netflix
Uber
Stripe
Microsoft
Apple
Airbnb
Google
Meta
Amazon
Netflix
Uber
Stripe
Microsoft
Apple
Airbnb
Google
Meta
Amazon
Netflix
Uber
Stripe
Microsoft
Apple
Airbnb
Who is Electi for?

Where you are now, and where you'll be.

Pick the track that sounds like you. Both cover algorithms, system design and getting in front of companies.

For you if:You want the whole preparation as a program, in a group and at a steady pace1+ year as a developer10–15 hours a week to prepare

Now

Lots of effort. No system.

Algorithms

You solve random LeetCode problems and forget them a week later.

System design

You've never designed anything bigger than your team's service.

Applying

Your resume gets filtered out, and you're not sure where to apply.

Preparation

No plan, just a feeling that you're behind.

Your 'progress'

Activity exists. Clarity doesn't.

After 7 months

A system that gets you ready.

Algorithms

You spot the pattern and solve mediums under time pressure.

System design

You can walk through scaling, caching and databases on real cases.

Applying

An ATS-ready resume, 5–10 STAR stories and a list of target companies.

Preparation

A weekly plan checked by a mentor, and progress you can see.

Your track

W1Arrays & Two Pointers
Confident
W9Trees & Graphs: BFS/DFS
Has gaps
M7System design: URL shortenerNow
HOW IT WORKS

How it works

From practice to placement, step by step.

Step 01

Platform & Analytics

Start by finding your blind spots. The platform analyzes your solving speed and patterns to create a personalized plan.

Step 02

Technical Foundation

Deep dive into algorithms. Solve 15-20 problems per week focusing on patterns, from Arrays to Dynamic Programming. Duration adapts to your starting level.

Step 03

System Design

Move from code to architecture. Learn scaling, load balancers, and databases using real-world cases.

Step 04

Resume & Behavioral

Package your experience for ATS, prepare STAR format stories, and conduct regular peer-to-peer mock interviews.

Step 05

Deployment Strategy

Build an effective mass-apply strategy. We help with applications, interview prep, and salary negotiation to maximize your offer.

Who you'll prepare with

Engineers with experience at Google, Meta, Bloomberg and Two Sigma. Each of them went through these interviews, and all three work in London.

Sultan Rzagaliyev

Sultan Rzagaliyev

Software Engineer @Meta

London, UK

Bloomberg LondonBloomberg NYGoogle Kraków

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.”
Almaz Zinollayev

Almaz Zinollayev

Machine Learning Engineer @Ex-Meta

London, UK

Ex-Lyft

Machine Learning Engineer at Two Sigma in London. Previously worked at Meta and Lyft, bringing extensive experience in building scalable ML systems and performance optimization.

Timur Umayev

Timur Umayev

Engineering Manager @Google

London, UK

Ex-MetaEx-Booking.com

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.”
Unfiltered feedback

Real progress. In their own words.

Messages from students currently in the program.

S
ShokhrukhFoundation · Month 2

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.

14:02
Y
YernurFoundation student

If 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.

15:45
G
GalymAccelerator student

Main 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.

18:30
R
RomanAccelerator · Month 3
Netflix Interview · Revolut Interview

3 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.

20:15
S
ShokhrukhFoundation · Month 2

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.

14:02
Y
YernurFoundation student

If 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.

15:45
G
GalymAccelerator student

Main 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.

18:30
R
RomanAccelerator · Month 3
Netflix Interview · Revolut Interview

3 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.

20:15
S
ShokhrukhFoundation · Month 2

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.

14:02
Y
YernurFoundation student

If 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.

15:45
G
GalymAccelerator student

Main 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.

18:30
R
RomanAccelerator · Month 3
Netflix Interview · Revolut Interview

3 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.

20:15
S
ShokhrukhFoundation · Month 2

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.

14:02
Y
YernurFoundation student

If 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.

15:45
G
GalymAccelerator student

Main 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.

18:30
R
RomanAccelerator · Month 3
Netflix Interview · Revolut Interview

3 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.

20:15
How to choose

One goal. Two formats.

Choose by how you like to prepare, not by level.

FoundationRecommended
Accelerator
Format
A small group of up to 8
1:1 with a personal mentor
Rhythm
A workshop every week and a mentor's written review every 2 weeks
2+ one-on-one sessions a month and mock interviews
Plan
The whole program: algorithms, behavioral, system design
Built around your own gaps
Getting hired
Help with applications and referrals
Referrals through mentors, help with the offer and 6 months of placement support
Length
7 months
12 months: 6 active + 6 placement
Price
from $129/mo
$300/mo + 10% Success Fee, only for an offer abroad
Goal
An offer at a Big Tech company
An offer at a Big Tech company
MENTORSHIP

Prepare with a mentor. All the way to the offer.

Two formats, one goal: prepare in a small group, or work 1:1 with a Big Tech engineer.

Group · 7 months

Recommended

Foundation

The whole preparation in a small group with a mentor.

For those who want the whole preparation as a program: a group, a weekly rhythm and a mentor's review. We recommend 1+ year of experience.

from$129/mo

when you pay for all 7 months up front · $150/mo month to month

Cohort enrollment: 6 of 8 seats taken · We start as soon as the group is full.

Learn more about Foundation

What you get

  • 1 workshop per week with material deep dives
  • Bi-weekly personal written feedback from your dedicated mentor
  • Access to private Electi community & mentor group chat
  • Behavioral interview preparation
  • Job application support & referrals

In 7 months you'll have

  • DSA patterns you can apply under interview pressure
  • System design basics: scaling, caching, databases, real cases
  • An ATS-ready resume and 5–10 STAR stories
  • A clear plan for where and how to apply
“My background was mostly ML and SQL. Now I'm getting through every topic we cover, while working full-time.”
Shokhrukh · Foundation · Month 2
Sultan Rzagaliyev
Almaz Zinollayev
Timur Umayev

Mentors: Sultan · Meta, Almaz · Ex-Meta, Timur · Google

1:1 · 12 months

Accelerator

Your own Big Tech mentor, all the way to the offer.

For those who want a personal mentor and a plan built only around their own gaps. We recommend 2+ years of experience.

$300/mo

12 months: 6 active + 6 of placement support

10% Success Fee, only if it works. Paid only after you accept an offer in the US, EU, UK or Canada. A local offer or no offer means nothing extra.

3 mentor slots left · Each mentor takes a limited number of students.

Learn more about Accelerator

What you get

  • From 2 one-on-one mentor sessions per month during the active phase
  • Fully personalized preparation plan
  • Mock interviews with feedback
  • Resume and application strategy
  • Coding + system design curriculum
  • Referrals and placement support in the second half
  • The Electi platform and community
“3 months in and I'm in interview loops at Netflix and Revolut. My mentor called the exact problem from the coding round.”
Roman · Accelerator · Month 3
Sultan Rzagaliyev
Almaz Zinollayev
Timur Umayev

Mentors: Sultan · Meta, Almaz · Ex-Meta, Timur · Google

Not sure which one? Foundation suits most people: the whole program, in a group and at a steady pace. Accelerator is for when you want a personal mentor and 1:1 work.

Big Tech pay

What Big Tech pays engineers like you.

Average yearly total compensation: salary, stock and bonus, before tax, in US dollars.

Google

$218K

L4 · London · ~5 yrs of experience

Meta

$191K

E4 · London · ~5 yrs of experience

Amazon

$186K

SDE II · London · ~6 yrs of experience

AppleApple

$173K

ICT3 · London · ~5 yrs of experience

Netflix

$131K

L4 · Poland · ~6 yrs of experience

Microsoft

$126K

SDE II 61 · London · ~3 yrs of experience

Bloomberg

$224K

Senior SWE · London · ~8 yrs of experience

For scale

The whole Foundation program costs $900. A Middle engineer at Google in London earns that before tax in about 1.5 days.

Source: levels.fyi, September 2026. Europe means London, or another European hub where London has too few reports for a level. Grey figures come from the other region.
FAQ

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