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
A program to help developers get a high-paying job through live classes where they learn the internals of how things work and master system design.
Live classes
Live classes for 9 months. Simple explanations for complex topics.
9 months program
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
9 months long online learning program to master AI and ML by learning the internals of how things work and mastering system design to get a high-paying job in AI.
View NowAndroid Development
9 months long online learning program to master Android Development by learning the internals of how things work and mastering system design to get a high-paying job in Android.
View NowYour Teacher and Mentor
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.
N-gram Speculation in LLMs
In this blog, we will learn about N-gram Speculation in LLMs, a simple trick that makes a language model write its answer faster by guessing the next few words from the text it has already seen. We will also see how an LLM writes one token at a time, why that is slow, what Speculative Decoding is, what an n-gram is, how N-gram Speculation guesses the next tokens by looking up the prompt, how the model verifies those guesses without changing the final answer, and where it works well and where it fails.
Prefill-Decode Disaggregation
In this blog, we will learn about Prefill-Decode Disaggregation, a way of running a large language model where the reading of the prompt and the writing of the answer happen on separate machines. We will also see how an LLM answers a request in two phases, what the KV Cache is, why the two phases need different things from the hardware, what goes wrong when both run on the same machine, how disaggregation solves it step by step, its advantages and disadvantages, and where it works well and where it is overkill.
KV Cache Compression
In this blog, we will learn about KV Cache Compression, the set of techniques that shrink the memory an AI model uses to remember the conversation while it is writing its reply. We will also see how an LLM writes one token at a time, what the KV Cache is and why we need it, why this cache becomes so huge, how quantization stores the same memory in fewer bits, how we can throw away the tokens that do not matter, how sharing across attention heads reduces the cache, how the cache can be squeezed into a small hidden vector, and when to use which one.
Chunking Strategies for RAG
In this blog, we will learn about Chunking Strategies for RAG, the art of cutting a big document into smaller pieces so that an AI system can find the right piece at the right time. We will also see what RAG is, why chunking is needed at all, what happens when we chunk badly, the most useful chunking strategies one by one, how to pick the chunk size and the overlap, and where each strategy works well and where it fails.
Vectorless RAG
In this blog, we will learn about Vectorless RAG, a way of answering questions from our own documents without converting those documents into numbers and without using any vector database. We will also see how the normal RAG works, why the vector part creates problems, how a Vectorless RAG system reads a document the way a human reads a book, how the tree structure and the search happen step by step, what the advantages and disadvantages are, and when to use which one.
How do Attention Sinks work?
In this blog, we will learn about how attention sinks work in Large Language Models. We will also see why streaming long conversations becomes a problem, why the first tokens become a sink that the naive fix breaks, and how StreamingLLM uses attention sinks in the real world to keep models running.