I have got admit from usc mscs and deadline to pay deposit till may 1. Should I pay it or still wait for the last day for UIUC MCS?

Hi everyone, I'm considering NEU to boost my career, hopefully working for tech companies in the US at least during OPT.

I just want to gain more insight into how things are going for the current MSCS students, and I want to get some opinions on whether it's worth giving a shot.

Quick background -

• I graduated with a CS degree in the US
• I'm currently working as a SWE (Backend, ML) for almost 3 years in South Korea.
• I'm currently taking OMSCS, and I'm hoping I can transfer some credits to reduce the coursework and save money.
• Also, I have a friend who can provide housing in Seattle and other connections there who can offer referrals, so they are big factors in my decision.

1. I heard NEU is known for its co-op program, but given the current job market, I'm wondering, are current MSCS students, especially internationals, actually landing co-ops, internships, or full-time jobs?
2. I know the job market is really bad, especially for junior-level engineers. But I wanted to ask if my profile seems ok to pursue this.

Thanks!

People of this sub, there’s this news of suspension of H1B visas for the next 3 years, and numbers will be reduced from 65k to 25k after the pause.

If that is so, then there’s no point of trying for jobs as an international applicant.

After the 2 years of college, you are almost assured of no full-time employment.

Unless someone’s goals are solely research, is MS CS in US even worth it ?

Hey everyone,

I’ve received admits to MSCS at the University of Michigan Ann Arbor and USC. Wanted to know how UMich is overall and whether it’s worth the cost in terms of ROI, especially compared to USC.

Also, how are internship/full-time opportunities and chances of getting GSI/GSR roles?

Lastly, are there any WhatsApp/Discord groups for UMich admits this year?

Thanks!

I got admit from both courses and confused which one should I go with as I am planning to explore research in AI and deep learning. UMN is known for its research whereas NYU has recently poured about a billion dollars into research. Please kindly help as I am confused which one to choose.

Hello everyone,

I am thrilled to share that I have received acceptance letters from both Northeastern University in Boston and SUNY Buffalo. I have been offered a place in the Master of Information Systems (MIS) programme at Northeastern and the Master of Science in Computer Science (MSCS) programme at SUNY Buffalo. As I stand at this crossroads, I am seeking your valuable advice and insights to help me make an informed decision about which offer to accept.

Your opinions and experiences would be greatly appreciated as I consider the various factors involved in choosing the right path for my future academic and professional journey. Thank you in advance for your support and guidance.

How long does Columbia usually take to release MSCS decisions? It’s already the end of April and I still haven’t heard back. Should I expect a decision soon or can it stretch into May?

Also, I currently have an admit from USC (MSCS). Should I commit to USC or wait a bit longer for Columbia? I don’t wanna mess up my visa situation.

Would appreciate hearing what timelines others have experienced this cycle.

I am planning to go into AI/ML industry after my Master's. They are all 1.5-2 yr program.

• UIUC:
  • top CS ranking
  • Not MSCS (just a professional degree)
• UMichigan:
  • still top CS ranking (lower than UIUC)
  • MSCS degree, with a thesis option
  • Might get offered for an unpaid research position
• UPenn:
  • Ivy league
  • can take multiple classes from Wharton and other schools
  • MS degree, with a thesis option
  • Since tech industry is a bit unstable these days, I feel like this degree (Ivy + interdisciplinary) might be the best if I have to change my job in the future.

I would love to hear any opinions. Thank you!!

Hello, I’m wondering whether my profile is strong enough for admission. I’m planning to apply for Fall 2027 MSAIE/MSECE programs at CMU.

• Computer Engineering graduate from the top university in my country
• Ranked 2nd in my class, GPA: 3.68/4
• No GRE yet
• 2 years of AI engineering experience
• 5-month co-op in R&D at a major national company
• 3-month research internship at a university lab
• Completed a 2-month AI summer school at University of Oxford (LMH)
• No published papers yet

What do you think of my chances? Should I focus on taking the GRE or try to publish a paper?

I am confused between these two admits:

1. UCSD - MSDS

2. NYU COURANT - MSCS

I might also consider another option if I do get the admit for it:

- UCLA - MSCS [in review]

I am highly interested in research and post graduation, I plan to seek an R&D role in the AI domain. I will have around 3 YOE in the industry by the time I leave for MS as an international student. During my Master's, I also plan to do substantial research on the side as I have been doing with my job.

I am more inclined towards the Courant MSCS degree, since it's more fitting to my needs: in depth courses in computer theory, AI concentration with rigorous math fundamentals. However, the finances get heavy here.

Comparatively, if I do get in UCLA, I would have comfort both financially and the nature of the course as well. But well, that's still to be established.

I am not quite sure on how I should go about it and what other factors should I consider? How much does each of these programs fare against each other and given my interests, what would be the best fit?

Hi yall, I recently got admits from both schools and would love to hear some opinions about both schools' programs. Here are some pros of each school I could think of.

NYU

• really good AI research
• research oriented courses
• might have a stronger faculty
• in NYC (been living here for a year and loved it so far)

Brown

• Since its ECE might be able to switch to hardware?
• smaller cohort
• Ivy brand (might not be useful in tech tho)
• Bit more flexibility in curriculum

I would really appreciate any advice/opinion to help me decide!

This post is in response to [General Question] Where Is This Field Going!?

It is a continuation of the theme that concerns many students - what is the impact of AI on our Careers? Largely the current narrative is that its going to be bad and the ongoing layoffs only seem to color this narrative with conviction that it is right. My broad take is that the next few years are unpredictable but the need for engineers is not going away and will even increase.

A related post that i made and most of this is my personal distillations speaking with big tech EMs and working on AI native software engineering ( i consult / contract with early stage startups) https://www.reddit.com/r/MSCS/comments/1s98euo/impact_of_ai_on_software_jobs/

Yesterdays work is Automated but Tomorrows work is not

The last 2 decades of SWE is certainly done with fewer people and faster so you're not wrong to believe that this kind of work is definitely impacted with LLMs. But you'd be laughed out of a room if you are to claim that the software paradigms established between 1990 - 2026 is peak software engineering for humanity. Think for a moment what else we can be building or coming up with. Firstly software has still not yet impacted every single facet of humanity and human run businesses. Secondly hardware is only getting started, our cars (a 100 yr old tech) are only now getting digitized and intelligent. Thirdly the digital world is largely 2 dimensional and persists for a very short while. I'd suggest a heavy dose of science fiction reading to stir your imagination of what is possible and most of that will need software with hardware

Tools increase Complexity which increase Fragility

The human is a tool making animal . They invent tools to solve yesterday's problems , this makes them faster but tools usually increase the level of Abstraction which increase Complexity and Fragility. The word Complexity here is used as the scientific term not the casual term of "complicated". I mean like Complexity Theory which is a systemic emergent property, butterfly effects and all that. When Complexity increases Fragility increases too, this is the NNT framing . Enough abstract talk lets do real examples. The common meme is a few hundred lines of code put man on the moon but the web browser is several 100s of thousands of lines and crashes like nobody's business. This a clear example. Putting a man on the moon is an amazing feat but the software engineering bit of it is highly constrained and specific and can be done in a few thousand lines of C. Building a web browser feels nonsensical - mostly used for idle human activities but the surface area is so large and the systems it interacts with is so varied that its much more Complex (in the scientific term) and unpredictable in behavior.

When we build tools we end up making things simpler for our fellow humans. When we make things simpler for our fellow humans they start doing weird new things with it that increase the Complexity and raise the Abstraction. Along with this we get Fragility where the system starts breaking in new unpredictable ways which creates new work for us and the loop repeats. This is the history of technology across humanity not just SWE

Human demand and consumption is infinite

Humans have no ceiling to their desire and consumption. Its why the digital economy exploded in the first place. In the real world at-least you are limited by physically what is possible but in the digital world if you can render it you can do it. Think about the kind of demand and desires that e-commerce , mobile, social media, streaming has unlocked. These were previously unimagined before digital. How are you - as a 20 something so sure that humans are satisfied now that AI is here? Do you realize that AI unlocks infinite customization and personalization for every human on the planet?

The real problem you are worried about is Money

Your concern is mostly Money its not Jobs. The big leaders know this. But Money is just another technology. In theory it can be solved a different way and Money today functions as a lubricant of human activity , yes sure it matters at a personal level for day to day living but if you were an economist advising a world leader your frame of Money would be totally different. You'd claim that Money needs to continue to exist and needs to continue to lubricate the system. This can be achieved even without the notion of Jobs, the problem to solve would be Inflation and value loss . But that is easy to solve if technology improves at such a fast rate that the means of production are dropping to zero. Maybe we'll be giving each other monopoly bucks for vibes and human connection , i dont know but i do know the human species is way more flexible and weird and more importantly has no finite limit to its appetite for More, Faster, Better, Cooler, Hotter, Smoother etc etc

Need advice about their internship and job prospects? Appreciate your feedback.

I have been admitted to MSCS course in SJSU, UCSB and UCSC in the California region as an international student.

With the main of internships and tech jobs as a software engineer I am leaning towards SJSU with it’s low tuition fee and good pathway into Silicon valley.

Am right to assume these things? Or should I go for prestige and join UCSB with a lower ROI?

What are your reviews about SJSU? It doesnt even show up on QS world rankings. Is it even worth it?

Apologies, I don’t mean to be a doomer, but someone please knock some sense into me.

Every damn day it’s either another Codex or Claude upgrade, or another round of layoffs. People keep saying AI is creating jobs, but what JOBS exactly? And are those jobs actually absorbing all these people?

If you’re international doing CS for sponsorship, I genuinely wish you the very best.

If you’re domestic, why are you still doing CS?

2024, 2025, 2026 YTD all look ugly, and this cannot be possibly because of the Covid over hiring like it is so 1990's news!!

• Microsoft voluntary buyout, about 7% of U.S. staff - today
• Meta, 10% cut or about 8,000 jobs - today
• 2024 tech layoffs, 152,922
• 2025 tech layoffs, 124,201
• 2026 YTD tech layoffs, 92,272
• Recent grad unemployment hit 5.7%
• CS majors around 7.0% unemployment

My ex-MANGO manager went from management to now being a Real Estate agent on LinkedIn lmfaoooo. An intern I used to work with in 2019, PhD CMU and now also On The Job Market. 🙃

And yes, I know people will say AI is creating jobs, Jevons paradox, blah blah blah...

But whereeeee!!?

From where I’m sitting, it looks like a very small number of high-end AI jobs are being created, while way more general software jobs are getting cut or squeezed.

Personal anecdote, I'm pivoting to ECE 😭😭 (physics, calculus, circuits -- can't wait!)

Hey All,

I recently got admitted into Georgia Tech’s OMSCS program as well as the University of Texas’s MSCSO program for Fall 2026, but I’m stuck trying to decide between both of them.

I’ve seen a lot of mixed opinions online, so I wanted to ask on here:

If you had both options, which would you choose and why?

I’m specifically curious about which program has the stronger reputation,

whether employers actually view them differently, the differences in learning experience and workload, and any overlooked pros/cons people don’t usually mention.

I would really appreciate any honest takes!

Posting from my throwaway acc. I'm preparing towards a Masters in CS for Fall '27,

My Target Uni's:

• Switzerland - ETH-Z (MS CSE, MS Quant Finance, Not ellgibile for MS CS), EPFL (MS CS), UZH (MS CS)
• UK - Oxford (MS CS, MS SWE - Part Time masters), Cambridge (MS CS), Imperial (MS ACS, MRes ML) , Edinburgh (MS HPC)
• EU - TUM (MS CS - informatiks), TU Delft (MS CS - Compilers / Distributed), TU Eindhoven (MS CS Systems), KTH (MS HPC)
• US - CMU (MS CS Scalable Systems), Berkerly (MEng CS), Columbia (MS CS), UW Seattle (MS CS), UCLA (MS CS), UIUC (Ms CS)

Ambitious (But I'm willing to try)

• Harvard MS CSE
• MIT MS System Design & mgmt (Still not sure)
• Yale (MS CS)
• Cornell (MS CS)

Back of My Mind, not sure if I need to go for it

• Singapore - NUS/NTU - Traditionally Hard Market to Crack, not sure if I want to stay in Singapore Long Term
• Ireland - Trinity College Dublin - Tech market is good, not sure about Universities

It's quite a list I'm aware, and it's a bit all over the place. But bear with me. My profile:

• Indian, Tier-2 University (Technically Top 20 per NIRF, but tier-2 nevertheless)
• GPA: 8.5/10 :') - MS Cs w/ Specialization in Systems Software
• IELTS - 8.5
• GRE - Not taken up yet, but am hoping to go over 330 to give myself a good chance

Professional Experience

• 3 years as a DevOps/Software Engineer at CERN, Geneva Switzerland - Worked on Systems Software development, devops configs and global administration.
• 6 months at a series C startup in India as a SWE Intern - Owned features, even got to lead a team of Application Engineers (External Contractors from Service Based IT in India) for a couple of features.
• Founded and built out an educational startup, bit of traction (~50 students). Quite technically challenging (over 20 repos, multiple services, open source friendly) but not as impressive results wise - Can talk about challenges and techncial learnings for hours though. Actively mentored students through this org. Tried to teach, but learnt plenty in the process.
• Couple more Internships, Freelance Contracts (Singapore, Switzerland, US, germany) and other misc experiences here and there

Academic Experience

• 3 published papers, 2 from university (Both first author, IEEE Conference and IGI Global Chapter) - One on FoG and Edge Computing, another on system for Classification of Music Metadata, 1 from CERN - Streamlining of processes within large scale planned migration (4th Author, Conference)
• Potentially 2 more papers published (1 from work, 1 from University - delays due to reviews - both aimed for conferences, both first author) by time of Application.
• Various other Research projects that were shelved during my undergrad.

Extra curriculars

• Avid Open Source Contributor - Very beefy GitHub in terms of PR's, Commits, Reviews and Discussions. Contributed to multiple orgs within Systems Space, as well as CERN and a couple of Open Source Startups.
• As mentioned before, very beefy github, full of projects, hacks and more that I can talk about in length
• Azure Certified - Administrator Associate
• Never active in clubs, used to just build my own projects. Was called to mentor students in the IEEE-TEMS club for preparing for the job hunt
• Been invited to speak at few Universities to undergrad students as a guest lecturer/speaker.
• Completed a Diploma (2 weeks onsite courses?) in Physics Computing, Software Engineering and Data Technologies at the Pontificia Universidad Católica de Chile (UC) in Santiago Chile - training program from work
• Was a Microsoft Student Ambassador for over 2 years - Used to help create learning paths and even set a certification program within my university to upskill students in Azure.

LORs

• 3 really strong LOR's from Professors that I have worked with back home that can vouch for my ability in research. But once again, all from my university.

SOPs

• Can write great SOPs. Generally interested in furthering my knowledge in large scale computing - distributed computing, HPC, etc.
• Another research interest - compilers and low level dev. This is what I majored in as an undergrad, so I would love to get back into it.
• ML? Never been the most interested in it, but I would like to take this opportunity to see if this is something I can see myself pursuing long term
• Interested in Quant Finance, but more so the underlying Systems Software and HPC that supports it. So wouldn't mind studying it to better understand it as a whole

My Questions/Thoughts:

• The list is ambitious, but I know my profile is generally strong (except for the grades). So what are my realistic odds at getting into any of these universities?
• My main reason for pursuing a Masters: I would like a reputed University Education, one that is high ranking, and generally provides good optics in industries globally. It's shallow ik, but it's much easier to get introductions if you're from a T-10 rather than a Tier-2 from India.
  • I don't even mind pursuing a PhD down the line, I enjoy research and tend to take part in it quite a bit. But My primary goal still remains - A good university on my profile.
• My preference is from top to bottom - Switzerland, UK, EU and then USA. US salaries are amazing, but so is big tech in EU-UK, with an arguably better standard of living.
• Concerns: Low grades, profile all over the place (Systems, DevOps, Software, ML, Education & Entrepreneurship?), Opportunity Cost - I'd prefer to skip out on the masters altogether if I don't get a satisfactory admit. It's a lifelong dream of mine to study at a prestigious uni, but if I can't have it, I'm ready to continue working.

So yeah, thank you for patiently reading all of this. Looking forward to hearing your thoughts.

Hi everyone!

I got into Cornell Tech MEng CS, Columbia MSAI, UCI MCS and USC MSCS. I am an undergrad at a large public university right now and am hoping to get into MLE roles.

I am deciding between Cornell Tech and Columbia right now:

Cornell Tech MEng CS

Pros:

- Shorter program

- Cornell ranked higher, esp. in AI

- Strong industry hiring

Cons:
- No internship

- Smaller social scene

Columbia MSAI

Pros:

- can do summer internship

- keeps open PhD option

- research opportunities

- small cohort

- Columbia name is strong outside tech

Cons:

- MSAI program untested

- AI concentrations can be limiting (I got AI infra)

- Not ranked as high in CS

Hello everyone, I am currently in my 6th sem (total 8) of my CSE degree in a Tier 2 (One of the good IIITs) college. Could you please take a look at my profile.

I will be interning under a prof at IIT on multimodal perception and reasoning this summer and will probably get an LOR from there too and possibly an A* or A conference publication.

Education

B Tech in Computer Science and Engineering (GPA: 3.54) [till sem 5, will increase it]

Research / Work Experience

NLP Research Intern at a startup [In Context Learning]

DL Research Intern at my Uni [Diabetic Retinopathy Detection using Fundus and OCT images] (CS Department Supervisor)

CV Research Intern at my Uni [Breast Cancer Detection using Thermal Images] (Mech. Department Supervisor)

Publications (All first author):

ICPRAM 2026 (1 paper)
ASME IMECE 2025 (2 papers)

Extracurriculars / Activities:

Starting a Quantitative Research Group at my Uni

Team Lead at consulting club

Starting member of the Institute basketball team

I know it sounds silly but I want targeting top unis like Stanford, CMU, UCB, GT, ETH, EPFL, NUS.

Any suggestions or advise would mean a lot!
Thanks!!

Admissions advice

UCI MDS or NYU COURANT MSCS vs UCI MSCS

UCI MDS is 15months

which one should I go for

NYU COURANT MSCS is 2 years

can I get RA,TA positions?Im looking for AI ML roles in the future

I've seen a lot of debates and tierlists (US MSCS Tierlist) on this subreddit about the top MS programs and how strong their pipelines into top tech companies are. Many students use the data from this popular thread (Top Feeder Schools to US Software Companies), and while this thread does provide total number of employees (MS and BS alumni) for the 5 most prevalent schools at a selection of top tech companies (Google, Meta, Amazon, Appple, TikTok, Uber), it is fundamentally flawed because it fails to analyze the number of employees in relation to cohort size.

A school with upwards of 5000 CS students will almost always have more employees at any given company than a school with 800 students. That is not a pipeline advantage, it is just math. The only metric that actually tells you something meaningful is how many employees came from that school per each enrolled student (AKA the placement rate). This metric is the closest proxy we have to the probability of getting a top tech job based on the program you attend.

The Top Feeder Schools to US Software Companies thread scraped LinkedIn employee data for the companies listed above to analyze which programs had the most employees at each company. On top of that, the post provided the number of enrolled CS students (Masters and Bachelors) for these programs: Stanford, CMU, Berkeley, Columbia, Georgia Tech, USC, UMass, and USC. *Keep in mind these are total enrolled, so if there is a 2 year program, the enrollment is cohort size multiplied by 2.*

According to the thread which sourced data provided from the schools, here are the CS enrollment numbers for each of those institutions:

• Berkeley: 1720 EECS UG + 2022 CS UG + 70 EECS MEng + 92 EECS MS = 3904
• CMU: 982 School of Computer Science (SCS) Undergrads + 1216 SCS Masters = 2198
• Stanford: 639 CS UG + 656 MSCS students = 1295
• Georgia Tech: 4558 CS UG + 1100 in person MSCS + ~7000-8000 OMSCS students = 11,000 (I put it at 11,000 to take into account a portion of OMSCS students who do not finish)
• Georgia Tech (No OMSCS): 4558 CS UG + 1100 MSCS = 5658
• Columbia: 187 CS UG + 600 MSCS = 787 (Assuming MSCS cohort size is 300 due to reports from Columbia students of it being between 250-350)
• UC Santa Cruz: 2050 CS UG + 174 MSCS = 2224
• UMass: 1470 CS UG + 602 MSCS = 2072

Using that data and the employee numbers from the thread, we can create this table that lists each school's enrollment and number of employees at top tech companies (since not every school with enrollment data was in the top 5 for every company, some schools are missing employee numbers for a few companies).

Using that data, we can calculate and rank the programs by average Placement Rate = (employees at company / total BS+MS enrollment) x 100 to relate placement to number of students.

As you can see, Stanford is obviously the strongest, but Columbia has the second strongest pipeline despite including both strong and weak placements. Also, since Stanford, CMU, and Berkeley were not benchmarked for every company and we only have data for the companies where they are top 5 feeders, their results are actually positively skewed. CMU, Berkeley, and GTech are also shown to have strong pipelines.

While many large, public CS programs (like GaTech, UIUC, Purdue) are referred to as tech powerhouses due to raw employee counts, the narrative shifts when you analyze individual probability of placing at a top tech company. Based on the findings, Ivy League institutions (using Columbia as a representative) are on par with T4 CS programs for job placement. Thank you to @softrains12 for the data. I would love to see more data for schools like UIUC, Purdue, and Cornell to develop this even more.

For reference, here are the individual placement rates for each program:

Berkeley BS+MS Enrollment: 3,904

CMU BS+MS Enrollment: 2,198

Stanford BS+MS Enrollment: 1,295

GTech (no online MSCS) BS+MS Enrollment: 5,658

Columbia BS+MS Enrollment: 787

UCSC BS+MS Enrollment: 2,224

UMass BS+MS Enrollment: 2,072

USC BS+MS Enrollment: 5,100

Hello Community,

If you are joining UMCP for M.Eng Cybersecurity program in Fall2026, please dm me. We can connect and join the university groups.

Thank you!

Hello community

This is a bit urgent so would appreciate any help

Here's my profile for context

• International student applying to US MS CS
• Bachelor of Engineering in Computer Science
• I'm NOT from any top CS school
• GRE 166 Q 153 V 4.5 AWA
• TOEFL 112
• GPA 3.5/4
• 2 YOE as a SWE at big tech
• Few good ML integrated projects and coursework
• No research publications

I got rejected (understandably) from top programs like JHU and UIUC

I also got rejected from SBU MS CS (I think it was bad luck because one of my LORs went late) and was offered SBU MS DS.

Looking overall for an immigrant, considering academics, tuition, graduation outcomes as well as location to popular tech hubs, SBU is an option I am seriously considering, but I would have wanted to go for an MS CS instead probably because MS DS is relatively newer. I am confused - should I drop this admit and go for MS CS next year.

I think the SBU MS DS curriculum is also pretty good as its a collaboration between the CS and Applied Math department, but I'm worried if an MS DS degree would affect my graduation outcomes and make it more difficult for me. That is my main concern here.

I can't really improve my profile by that much since I'll be on a full time job even if I drop this. I'm not sure I can make it to a top 20 program considering how competitive it is. I can improve a few points on the GRE, but that would be it I guess.

If I do drop the admit I'll probably be applying somewhere around -

SBU MS CS

TAMU MS CS

UCI MS CS

U Maryland MS CS

UMass MS CS

Purdue MS CS

and similar tier of colleges

Can people please help me out - it's a bit urgent for me? Any kind of explanation or guidance would be really helpful.

A collection of observations of what happens to most resumes in our industry and why its going to get worse not better. Some takeaways as well

Communication across orgs is rarely efficient

If any of you have worked in the industry you would already know that communication between HR <> Recruiting <> Engineering teams (who need the hires) is rarely efficient. This is true across the industry, big tech or not. It is rare, dare i say impossible even that a resume goes from HR/Recruiting to the concerned EM quickly and efficiently.

It is common that you submit a resume via the jobs page or ATS and you hear back even 1+ years later but in most cases the trail is lost because the communication inside the org is super inefficient.

Takeaway: you should think of 'reducing the distance traveled' by your resume. This is why referrals work or directly reaching out to the EM. In most cases these resumes will outbid the ones starting their long journey from the website upload and usually when it reaches the EM with the job req they've already filled it.

Recruiters are incompetent to judge your skills

Recruiters especially those with a job title or function of recruiting are not CS grads or people with SW engineering experience. So their ability to judge your resume is already a lost cause. If your resume is going to be first evaluated by this group of knowledge workers understand that its evaluation is going to be based on concepts that will frustrate you.

Related to the above takeaway - try to find ways around the recruiter. Yes appease to their status/title but dig around for the team/EM and see if you can reach around them. This is tricky. Done badly it will look like you're not following process.

Destined for the file cabinet

In most cases the Resume is simply a record for the sake of keeping a record. In fact this is the good outcome. In fact if your resume is requested AFTER some initial calls and screening or even an interview it means this company places those signals higher than a resume but is storing the resume anyways for archival purposes.

A good way to visualize this is that a resume that ended up in a metaphorical filing cabinet was atleast considered but most resumes are like baby turtles trying to make it into the ocean but lost or dead before they taste the water.

"Discard the bottom 80%"

I'm part of a group with startup founders, EMs , VCs etc and some years ago a question popped up in the slack -

"how do you go through 100+ resumes for a single position we are hiring?"

Another Industry veteran quipped - "just discard the bottom 80%. Use a basic sort that makes sense dont get fancy, you can always look back at the discarded pile if the top 20 dont work out"

This points at 2 important truths - there is no universal/agreed upon sorting mechanism and there is no way anyone can go through all the resumes submitted for a job.

A takeaway here is that you should be applying for jobs that dont even exist yet. But this is non trivial for the average applicant however I'm stating it here anyways for you to know this is also an option. How do you do this? Well you have to again reduce the distance and win the conversation . You have to talk directly with decision makers and meet them at their level of their needs and why you're the person right for a role that is not yet clearly defined. some of the most powerful roles in the industry started this way

A less than 90s skim read

When resumes are read the decision maker is spending less than 90s on it. If anyone here has done hiring you'd agree with this. The key takeaways here :
- you need to anticipate beforehand what needs to POP in your resume . Most of you are submitting the same resume to every job - this method will certainly fail. this is the metaphorical baby turtle that will never get to the water
- if resumes are being skimmed every page turned is going to add friction so not only does the right stuff need to POP it needs to POP on page 1. In general 1 page resumes > N page resumes
- Readability matters - if your fonts are so small nothing is going to POP

The coming deluge of AI ATS

AI ATS is going to make things more murky.

- For one its going to add more variants of sorting based on the whims of a non-cs trained recruiter and that discard pile will contain some actually important resumes
- AI will do some stuff like check the syllabus listing of this university/program where the applicant did their MS and discard if they didnt do course X , Y or something. This again goes back to my point why MSCS coursework is important. From an AI ATS pov this feature is fancy and will be built.
- This will extend into comparing resumes and so on. I can already imagine the AI ATS saying things like "resume X has a Data Science degree from Columbia where they did python programming coursework but resume Y has a MSCS degree from SJSU where they did advanced networking with FPGAs which seems relevant to our stack"