Get a High-Paying Tech Job
Software engineers like you join Outcome School to achieve the outcome that is a high-paying tech job.
Designed for Outcome
Programs to help developers get a high-paying job by learning the internals of how things work and mastering system design.
Recorded Classes
Self-paced recorded classes for AI and ML, and Android. Simple explanations for complex topics.
Two Programs
You can choose from "AI and Machine Learning" or "Android".
Learn from Anywhere
As this program is completely online, you can join it from anywhere in the world.
We teach Internals
To get a high-paying tech job, you must know the internals and be great at system design. Knowledge comes to those who crave for it.
Eligibility
Working professionals - anyone already working in tech but looking for a high-paying tech job by learning the internals and System Design.
Programs
You can choose anyone from the below.
AI and Machine Learning
100 hours of recorded classes to master AI and ML at your own pace by learning the internals of how things work and system design to get a high-paying job in AI.
View NowAndroid Development
70 hours of recorded classes to master Android Development at your own pace by learning the internals of how things work and system design to get a high-paying job in Android.
View NowYour Teacher
Amit Shekhar
Founder @ Outcome School • IIT 2010-14 • I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.
Our Students' Growth
Got Salary Hike
CTC Change: 4 LPA → 24 LPA, 9 LPA → 24 LPA, 13 LPA → 46 LPA, 20 LPA → 60 LPA
Notable Transition
Software Engineer → VP of Engineering, Software Engineer → Staff Engineer
Open Source
Worked on Open Source projects and received interview calls from Top Companies
Our students got placed in top companies thanks to their efforts.
What students are saying about us
Kiran Rao Chavan
Hi Amit, with every class that I see and hear from you, silently learning the techniques of system design skills in developing AI from scratch is phenomenal and I was impressed with "Attention is all you need" Paper explanation was next level. I have read YOLO Paper for image processing and writing python scripts after multiple attempts, but the way you give real time examples and the analogies that you give while explaining toughest things - you make it look like a cake walk. KVCache, Paged Attention, vLLM, Quantization, each and every concept that you teach is exceptional.
Aman Shekhar
Outcome School delivers deep, practical AI/ML mastery that standard degree programs simply cannot match. Under Amit’s expert supervision, the curriculum shifts you from an AI consumer to an AI builder, instead of just using tools like Claude, you build autonomous AI agents, build LLMs from scratch, playing with KVCache, code Transformer architectures line by line etc. The best part was understanding the paper "Attention is all you need", not only that now I can read and understand any whitepaper. This rigorous, code-first approach has completely transformed my career trajectory, elevating me to the official AI Focal Point in my organization and the go-to expert for all complex technical queries.
Khush Panchal
Amit has been an incredible mentor to me. Under his guidance, I navigated the world of open source, which took my journey to the next level. Amit's presence works as a catalyst in the journey of learning and growing. His insights were incredibly helpful, whether it was cracking firms like Microsoft and Blinkit, negotiating salaries, or making career decisions. His mentorship also enabled me to create major open-source libraries. I am grateful to have Amit as a lifelong mentor and look forward to creating a positive impact with him.
Recent Blogs
We publish high-quality blogs regularly for our learners.
How does an Android Developer transition to AI Engineer?
An Android Developer transitions to AI Engineer by keeping the Kotlin, async, networking, and shipping skills they already have, and adding Python, LLM APIs, RAG, agents, evaluation, and backend deployment on top of them, one project at a time with a coding agent.
How does the Bias-Variance Tradeoff work?
The Bias-Variance Tradeoff is the balance between a model that is too simple to learn the real pattern (high bias) and a model that is so sensitive that it learns the random accidents in its training data (high variance). A good model sits in the middle, where the total error on new data is the lowest.
How does a voice agent tell a real interruption from a cough or background noise?
A voice agent tells a real interruption from a cough or background noise by passing every sound through a series of checks, and it stops talking only when the sound passes all of them. It removes its own voice and the noise, then checks if the sound is human speech, lasts long enough, and contains real words.
How does a Software Engineer transition to AI Engineer?
A Software Engineer transitions to AI Engineer by reusing the engineering skills that already exist, learning how LLMs, prompts, RAG, agents, and evaluation work, and building a few AI products with a coding agent to prove it.
How do you become an AI Engineer?
To become an AI Engineer, we learn to build products on top of existing AI models using APIs, RAG, agents, evaluation, and deployment. We prove those skills with a few working projects that we build with a coding agent and can explain end to end.
How does Data Augmentation work?
Data Augmentation is a technique where we create new training examples from our existing data by making small changes to it, like flipping an image or rewording text.