Target audience: 2023 to 2026 grads.

Taking inspiration from https://leetcode.com/discuss/compensation/6108545/Journey-From-6-LPA-To-60-LPA & writing about my journey. Also I would add some references that helped me grow.

I am 2020 grad from a tier 2 state level college (All NITs, IIITs, BITs, new IITs, state level top colleges are tier 2 to me). During college days, I expertised in ML & DL.

Sept 2020: I started my journey in a data consulting company in a Data science + Data engineering role. Salary: < 9 LPA.
During my stay here, I started studying DSA. Participated proper hackathons where I understood how to make CRUD apps & how backend & frontend interacts. I also learnt about databases. I was rejected by Amazon badly around May 2021.

Study materials used by me to switch to next role:

1. DSA: Many youtubers.
2. ML: Coursera (deeplearning.io), articles on internet. Many youtubers are there.

Dec 2021: Joined a product company as ML Engineer. Salary: 15-18 LPA range. I started studying more about ML & DL. This was the time when GPT 3 was getting popular & I deep dived the world of NLP. Also I was practicing DSA passively.
I decided to switch because alot of layoff news were coming in May 2022 & I wanted to increase my pay before market shuts.

Study materials used by me to switch to next role:

1. Designing data-intensive applications by Martin Kleppmann
2. Designing Machine Learning Systems by Chip Huyen
3. DSA: https://www.youtube.com/@takeUforward
4. Data engineering: PySpark, Hadoop concepts on internet.
5. ML: Many youtubers, can refer https://www.youtube.com/@statquest for fundamentals. Also multiple blogs on internet.
6. Famous research papers related to computer vision & NLP.

Dec 2022: I finally joined a good product company & failing in a lot of interviews because of bad market conditions. My role here was MLOps & Data engineering. Salary: 35 LPA range with 23-25 fixed + RSUs & bonus.
Here , for the first time, I understood how real time ML inference services works & how we scale it to serve huge customer base. My data engineering knowledge base also increased exponentially. Parallely, LLMs came to limelight in 2022 & I deep dived into the world of LLMs.

Jan 2024: I got interview call for Google for SWE, MLE, L4 role. I started focusing more on DSA, ML System design & ML concepts ranging from classical algos to LLMs.

Study materials used by me to switch to Google:

1. ML System design: https://bytebytego.com/intro/machine-learning-system-design-interview + more which I got through my work.
2. ML concepts list: https://shubajit.notion.site/SWE-MLE-Google-FAQs-c3f87ea136dd4c779645ee22c6b96779 + more which I got through my work.
3. DSA: https://www.youtube.com/@takeUforward, same as before. You can refer to any source of your choice.
4. LLM basics: https://www.coursera.org/learn/generative-ai-with-llms
5. LLM advanced: Read research papers & articles.

Aug 2024: Joined Google. Pay: Standard L4 pay that you see on any other recent leetcode compensation post.

Dec 2024: I am facing a challenging*WLB here, but pay & food is great. Also people are very smart.

Thanking leetcode community which is continuously helping me grow.
Also thanking leetcode compensation section which always made me feel that their is still scope of betterment (somedays made me feel that I am broke).

Message to someone who is 4 years younger to me: Hustle 70 hours a week. Not entirely for office work, study for your growth 50% of the time.