If you're a student or a recent graduate in the US navigating this crazy job market, hang in there, more power to you! 🚀

I was lucky to bag 3 intern offers as well as 3 full-time offers last year, 4 of these being FAANG+. Achieving this meant applying for 100s of roles a week, no kidding. To find the right set of roles as soon as they dropped, I wrote a Python script to scan all Greenhouse job boards and catch them at scale. I'm sharing the live gsheet with 600+ open intern and new grad roles (SWE, AI, Quant/Finance, PM, Hardware), more than a 75 of these opened up just this week!

It updates daily so you have a clear target list every day! I plan on adding Workday and Ashby to the sheet soon too.

How I optimized my job searches

Having fresh job leads matters, and the three massive bottlenecks I figured out while going down the ATS rabbit hole:

1. Timing is everything. The data shows that roughly 80% of offers go to people who apply within the first 7 days of a listing.

2. Semantics matter way too much. I was applying for "AI Engineer" roles with "Machine Learning Engineer" on my resume. ATS parsers can be incredibly rigid. Literally just changing my past titles and headline to exactly match the target role had noticeably more callbacks.

3. Keyword stuffing backfires. Dumping keywords might get you past the initial ATS screen, but human recruiters will shoot it down with zero mercy. You have no choice but to actually embed exact phrases naturally into your bullet points.

How YOU can optimize your job search

Now doing all this'd mean barely 6 applications in an hour, worst part is this work doesn't make you learn much either. I'd highly automating most if not all of this process. Happy to answer any questions in the comments about my experience, my findings on ATSes or how you can automate the process in DMs/comments!

If you're a student in the US managing your college classes and this crazy job market at the same time, hang in there, more power to you!🫡

I was lucky to bag 3 intern offers as well as 3 full-time offers last year, all thanks to applying for 100s of roles a week. To find the right set of roles as soon as they dropped, I wrote a Python script to scan all Greenhouse job boards and catch them at scale. I'm sharing the live gsheet with 600+ open intern and new grad roles (SWE, AI, Quant/Finance, PM, Hardware), more than a 100 of these opened up just this week!

It updates daily so you have a clear target list every day! I plan on adding Workday and Ashby to the sheet soon too.

How I optimized my job searches

Having fresh job leads matters, and the three massive bottlenecks I figured out while going down the ATS rabbit hole:

1. Timing is everything. The data shows that roughly 80% of offers go to people who apply within the first 7 days of a listing.

2. Semantics matter way too much. I was applying for "AI Engineer" roles with "Machine Learning Engineer" on my resume. ATS parsers can be incredibly rigid. Literally just changing my past titles and headline to exactly match the target role had noticeably more callbacks.

3. Keyword stuffing backfires. Dumping keywords might get you past the initial ATS screen, but human recruiters will shoot it down with zero mercy. You have no choice but to actually embed exact phrases naturally into your bullet points.

How YOU can optimize your job search

Now doing all this'd mean barely 6 applications in an hour, worst part is this is entirely a waste of time. We're supposedly approaching AI and it's about time we automate this whole loop. I've built that ** automates all of the grunt work of a job hunt** and has already saved 9+ hours for 7 users. Sign up for the waitlist right away!

Happy to answer any questions in the comments about my experience, my findings on ATSes or my product in DMs/comments!

If you're managing your college classes and this crazy job market at the same time, hang in there, more power to you!🫡

I was lucky to bag 3 intern offers as well as 3 full-time offers last year, all thanks to applying for 100s of roles a week. To find the right set of roles as soon as they dropped, I wrote a Python script to scan all Greenhouse job boards and catch them at scale. I'm sharing the live gsheet with y'all, it has 550+ open intern and new grad roles (SWE, AI, Quant/Finance, PM, Hardware).

It updates daily so you have a clear target list every day! I plan on adding Workday and Ashby to the sheet soon.

How I optimized my job searches

Having fresh job leads matters, and the three massive bottlenecks I figured out while going down the ATS rabbit hole:

1. Timing is everything. The data shows that roughly 80% of offers go to people who apply within the first 7 days of a listing.

2. Semantics matter way too much. I was applying for "AI Engineer" roles with "Machine Learning Engineer" on my resume. ATS parsers can be incredibly rigid. Literally just changing my past titles and headline to exactly match the target role had noticeably more callbacks.

3. Keyword stuffing backfires. Dumping keywords might get you past the initial ATS screen, but human recruiters will shoot it down with zero mercy. You have no choice but to actually embed exact phrases naturally into your bullet points.

Now there are tools you can use to automate most of these things. Even I'm building one to automate all of it under one roof. Happy to answer any questions in the comments about my experience, my findings on ATSes or my product in DMs/comments!

Hey r/csMajors. I walked away from a solid SWE career to do a Master's in AI, thinking my prior YOE plus a degree would make the job hunt a breeze. I was completely wrong.

To help me survive this market, I wrote a Python script to scan all Greenhouse job boards and catch roles the second they drop. I'm sharing the output sheet with y'all. It's a live gsheet with 700+ open intern and new grad roles (SWE, AI, Quant/Finance, PM, Hardware). It updates daily so you don't waste time on dead reqs.

Here is why having fresh job leads matters, and the three massive bottlenecks I figured out while going down the ATS rabbit hole:

1. Timing is everything. The data shows that roughly 80% of offers go to people who apply within the first 7 days of a listing. I was wasting hours manually applying to stale jobs on LinkedIn that already had thousands of applicants.

2. Semantics matter way too much. I was applying for "AI Engineer" roles with "Machine Learning Engineer" on my resume. ATS parsers can be incredibly rigid. Literally just changing my past titles and headline to exactly match the target role bypassed the filter with flying colors.

3. Keyword stuffing backfires. Dumping keywords might get you past the initial ATS screen, but human recruiters will shoot it down with zero mercy. You have no choice but to actually embed exact phrases naturally into your bullet points.

Full transparency on this next part: My scripts worked so well that my friend and I are trying to build it into a startup. We wrapped it into a web app called Scyllus AI that finds fresh jobs, uses knowledge graphs with frontier LLMs to perfectly tailor your resume to the ATS (ngl, there is a stark difference between our output and a simple ChatGPT wrapper), and auto-applies for you.

We are running a free beta because our goal is to help people in tough spots. However, we are bootstrapping this with our own money and startup credits. To avoid going bankrupt on LLM API costs, and to make sure the platform stays bug-free, we have to use a waitlist to onboard people slowly.

If you want to help us test it out, you can join the waitlist by filling this quick 1-min google form.

We plan on adding Workday and Ashby to the sheet and Scyllus AI soon. Either way, the Google Sheet is totally free and ungated. Happy to answer any questions in the comments about how the ATS parsing works under the hood!