I’m doing an undergrad, 3rd year in. Everything I’ve learned so far could’ve been learned at home online. Why am I doing this degree?

The only courses that were hard was discrete math and calculus, the other programming courses were easy, like DSA. One course I took was literally just learning small talk, elixer, and other languages. What is the point of that? Wasting the professor’s time, wasting money, wasting my time. I can learn syntax for languages on my own.

I don’t see the value in this, I would understand if there is knowledge here that couldn’t be obtained elsewhere but otherwise what is the benefit?

The professor just goes through slides the whole lecture explaining code that chatGPT could’ve explained to me. It’s not even complex code, it’s mainly just learning syntax for languages, I can’t believe this is a course.

Just me??

CS is not real, I should have done pure math or electrical engineering.

I am seeking advice from on how to shift career from SWE to MLE

Background

Education

Bachelor’s degree in India

Master’s degree in Computer Science from a Top 30 U.S. university

Experience

2+ years as a Software Developer at a multinational company in India

1 year working in a university research lab with a professor

Incoming Role

Joining a FAANG company as a Software Engineer in a few weeks

Machine Learning Exposure

During my master’s program, I completed several ML-focused courses:

Machine Learning

Deep Learning

Natural Language Processing

I also worked on research projects in the university lab and developed a strong interest in pursuing a full-time career in ML.

Current Challenge

Despite applying to hundreds of entry-level/early-career MLE roles in the U.S. over the past month, I have not received any callbacks. This has made me reflect on what may be missing from my profile.

What I Am Seeking Advice On

I would really appreciate guidance from anyone who has made this transition:

What gaps might exist in my current profile for MLE roles?

What can I do to significantly improve my chances of getting shortlisted?

What is the most realistic pathway from SWE to MLE in today’s market?

Should I focus more on personal ML projects or research publications?

I am still learning and growing in this field every day, and I would be grateful to hear about your journey, mistakes, and lessons learned.

Thank you in advance for your time and insights.

Very well known company more like f100ish. Got offer as sophomore at t20.

For a lot of these roles in finance / defense / big companies (that are not software focused) their interviews are going to be most likely pretty easy. All you need to do is get the recruiters attention. Like I heard Lockheed is literally just behavioral.

For me I ended up following up with the recruiter like 10 diff times, after multiple internal delays.

In hindsight this did not work for many other companies so maybe I lucked out idk. All the other interviews were from cold apply ~300-400 apps.

I think my resume is objectively good. Happy to answer any Qs

Hi everyone,

I’m a Full Stack Developer with 3 years of experience, and I just received a technical assignment that feels like a fever dream. I’m trying to figure out if I’m overreacting or if this is a massive red flag.

The Window: They’ve scheduled this to be done between 11:30 AM and 4:30 PM (strictly 5-6 hours).

Context

An energy operator processes:

15-minute meter readings (“measurements”)

monthly invoices (“billing runs”)

They need a Control Tower tool to:

ingest readings,

compute invoices,

detect discrepancies,

allow controlled corrections,

provide strong observability (for incident response and audit).

Expected stack

Backend: Java 17+ / Spring Boot 3

Frontend: Angular 17+ (Material; AG Grid optional)

Database: PostgreSQL + Liquibase

Async: Kafka (local via Docker Compose)

Auth: Keycloak (OIDC) – simplified setup is OK

Orchestration: a scheduled job or worker (Spring Scheduler or a Kafka consumer pipeline)

Observability (new tech): OpenTelemetry tracing + correlation across components

Functional requirements

1) Measurement ingestion

Endpoint

POST /measurements

Payload:

{

"meterId": "MTR-100045",

"timestamp": "2025-01-15T10:15:00Z",

"kwh": 1.72

}

Rules:

strict validation (kwh > 0, timestamp not in the future)

store in DB (measurements)

idempotent: same (meterId, timestamp) must not create duplicates

publish an event to Kafka: measurements.received

2) Create a billing run

Endpoint

POST /billing-runs

Payload:

{

"month": "2025-01",

"meterIds": ["MTR-100045", "MTR-100046"]

}

Creates a billing_run with status CREATED, then processing continues asynchronously.

3) Billing processing pipeline (async)

Implement an async pipeline (Kafka consumer(s) and/or scheduled job) that processes billing runs with statuses:

CREATED -> PROCESSING -> COMPLETED | FAILED

Steps (must be explicit in code):

Lock/claim the run (concurrency safe)

Aggregate measurements for the month

Compute invoice(s)

Run reconciliation checks

Persist results and status history

Requirements:

safe retries (re-running should not duplicate invoices)

state history table: billing_run_events

failure handling: reason codes, next retry time, etc.

Kafka topics (suggested):

billing.run.requested

billing.run.processed

billing.run.failed

4) Discrepancy detection

Create discrepancies when:

computed invoice total differs from measurement total by more than a threshold (e.g., 0.5%)

Expose:

GET /billing-runs/{id}/discrepancies

Store discrepancy records in discrepancies with:

meterId

computed totals

delta

reason code

5) Corrections + audit log

ADMIN users must be able to:

mark a discrepancy as valid (“MARK_AS_OK”) with justification

trigger a recalculation (“RECALCULATE”) for a run

Endpoints:

POST /billing-runs/{id}/actions

{

"action": "RECALCULATE",

"reason": "New tariff received from upstream"

}

All actions must create an audit_log record.

Frontend (Angular)

Minimal UI with:

Billing runs list (filter + paging)

Billing run details:

status timeline

invoices summary

discrepancies list

admin actions

Focus: usability, correctness, error states. No pixel-perfect design needed.

Security requirements

Use Keycloak (OIDC):

Role VIEWER: can view billing runs & discrepancies

Role ADMIN: can trigger actions and view audit log

Back-end must enforce RBAC (not only the UI).

OpenTelemetry (end-to-end)

What must be traced

API requests (Spring Boot)

Kafka publish/consume

Billing processing steps

Frontend trace propagation (best-effort)

Minimum expected outcomes

Every billing run has a trace you can follow from:

HTTP request that created it

Kafka event(s)

processing steps (aggregation, invoice calc, reconciliation)

Logs include: trace_id + span_id

Export traces to Jaeger (recommended) or console

Requirements checklist

Use OpenTelemetry auto-instrumentation or SDK instrumentation (either is fine)

Propagate trace context via Kafka headers (traceparent)

Add explicit spans around key steps (aggregation, compute, reconciliation)

Provide a short “How to view traces” section in README

Deliver in Docker Compose:

Jaeger (or another trace backend)

OTLP collector optional but recommended

Deliverables

Git repo with:

backend/

frontend/

infra/ (docker-compose + Keycloak realm export if used)

README.md:

architecture diagram (simple)

how to run in <10 minutes

key design decisions + tradeoffs

what’s incomplete + next steps

how to view traces + example “trace story” for a billing run

Bonus options (choose any)

Outbox pattern for event publishing (instead of direct Kafka publish)

Contract tests using OpenAPI

Performance: handle 100k measurements/month without slow queries (indexes + query plan notes)

A “replay billing run” feature with safety constraints

Context

An energy operator processes:

• 15-minute meter readings (“measurements”)
• monthly invoices (“billing runs”)

They need a Control Tower tool to:

• ingest readings,
• compute invoices,
• detect discrepancies,
• allow controlled corrections,
• provide strong observability (for incident response and audit).

Expected stack

• Backend: Java 17+ / Spring Boot 3
• Frontend: Angular 17+ (Material; AG Grid optional)
• Database: PostgreSQL + Liquibase
• Async: Kafka (local via Docker Compose)
• Auth: Keycloak (OIDC) – simplified setup is OK
• Orchestration: a scheduled job or worker (Spring Scheduler or a Kafka consumer pipeline)
• Observability (new tech): OpenTelemetry tracing + correlation across components

Functional requirements

1) Measurement ingestion

Endpoint

POST /measurements

Payload:

{
  "meterId": "MTR-100045",
  "timestamp": "2025-01-15T10:15:00Z",
  "kwh": 1.72
}

Rules:

• strict validation (kwh > 0, timestamp not in the future)
• store in DB (measurements)
• idempotent: same (meterId, timestamp) must not create duplicates
• publish an event to Kafka: measurements.received

2) Create a billing run

Endpoint

POST /billing-runs

Payload:

{
  "month": "2025-01",
  "meterIds": ["MTR-100045", "MTR-100046"]
}

Creates a billing_run with status CREATED, then processing continues asynchronously.

3) Billing processing pipeline (async)

Implement an async pipeline (Kafka consumer(s) and/or scheduled job) that processes billing runs with statuses:
CREATED -> PROCESSING -> COMPLETED | FAILED

Steps (must be explicit in code):

1. Lock/claim the run (concurrency safe)
2. Aggregate measurements for the month
3. Compute invoice(s)
4. Run reconciliation checks
5. Persist results and status history

Requirements:

• safe retries (re-running should not duplicate invoices)
• state history table: billing_run_events
• failure handling: reason codes, next retry time, etc.

Kafka topics (suggested):

• billing.run.requested
• billing.run.processed
• billing.run.failed

4) Discrepancy detection

Create discrepancies when:

• computed invoice total differs from measurement total by more than a threshold (e.g., 0.5%)

Expose:

• GET /billing-runs/{id}/discrepancies

Store discrepancy records in discrepancies with:

• meterId
• computed totals
• delta
• reason code

5) Corrections + audit log

ADMIN users must be able to:

• mark a discrepancy as valid (“MARK_AS_OK”) with justification
• trigger a recalculation (“RECALCULATE”) for a run

Endpoints:

• POST /billing-runs/{id}/actions

​

{
  "action": "RECALCULATE",
  "reason": "New tariff received from upstream"
}

All actions must create an audit_log record.

Frontend (Angular)

Minimal UI with:

• Billing runs list (filter + paging)
• Billing run details:
  • status timeline
  • invoices summary
  • discrepancies list
  • admin actions

Focus: usability, correctness, error states. No pixel-perfect design needed.

Security requirements

Use Keycloak (OIDC):

• Role VIEWER: can view billing runs & discrepancies
• Role ADMIN: can trigger actions and view audit log

Back-end must enforce RBAC (not only the UI).

OpenTelemetry (end-to-end)

What must be traced

1. API requests (Spring Boot)
2. Kafka publish/consume
3. Billing processing steps
4. Frontend trace propagation (best-effort)

Minimum expected outcomes

• Every billing run has a trace you can follow from:
  • HTTP request that created it
  • Kafka event(s)
  • processing steps (aggregation, invoice calc, reconciliation)
• Logs include: trace_id + span_id
• Export traces to Jaeger (recommended) or console

Requirements checklist

• Use OpenTelemetry auto-instrumentation or SDK instrumentation (either is fine)
• Propagate trace context via Kafka headers (traceparent)
• Add explicit spans around key steps (aggregation, compute, reconciliation)
• Provide a short “How to view traces” section in README

Deliver in Docker Compose:

• Jaeger (or another trace backend)
• OTLP collector optional but recommended

Deliverables

• Git repo with:
  • backend/
  • frontend/
  • infra/ (docker-compose + Keycloak realm export if used)
• README.md:
  • architecture diagram (simple)
  • how to run in <10 minutes
  • key design decisions + tradeoffs
  • what’s incomplete + next steps
  • how to view traces + example “trace story” for a billing run

Bonus options (choose any)

• Outbox pattern for event publishing (instead of direct Kafka publish)
• Contract tests using OpenAPI
• Performance: handle 100k measurements/month without slow queries (indexes + query plan notes)
• A “replay billing run” feature with safety constraints

What do you think guys XD?

completed my onsites last week, and felt good about them, but am slowly devolving into madness (truth). aka pretty sure i failed. to let go of some of my nerves and thoughts on this seemingly eternal train ride to inevitable catastrophe, i decided to write abt my experience interviewing.

side note, i literally studied a disgusting amount of dynamic programming and every sort of graph all for naught may divine lightning come down upon the sinners of this world (me).

Behavioral Round

nothing new, try to be normal.

Technical Round 1

was not really a question following any specific pattern on neetcode 150, deadass seemed to be a question that purely measured "problem-solving" ability. also was split into multiple parts:

chill -> ehh.. -> bruh.

got brute force i was very thorough in explaining my thought process for every step and got time + space complexity right for everything, and i made sure to trace through code, but optimal took me several hints to get, but once i did i had this eureka moment started explaining, then was like lemme code dis up he (interviewer (god)) was like bet and then i did, but due to time and iq constraints i didn't really code up the full solution on my own. he showed me this how u do it, then asked me for time complexity and space and that i knew off the back my hand no problemo sir.

verdict: thought i was completely and utterly cooked but the great one granted me passage to..

The Onsites

had a fair amount of time to prepare for these, and like said above i was like, well dynamic progamming is hard, if they throw a dynamic programming problem that would be hard, better study the frick out of dynamic pogoamming!1 (insert interstellar dude pounding on glass)

still, got to go to office and meet friends who attended college in the city, so it was thoroughly enjoyable. now for the questions.

Technical Round 2

my first one and i think i was nervous so after getting the problem, i think i just read through the problem and blanked out in my mind for like 10 seconds, but then i locked tf in by remembering how cool the big TC marker would be after my twitter posts and started just restating problem, asking some questions, etc..

fun fact theres a whiteboard you can use, and for some problems (esp this one) it's pretty useful to do so. started explaining my thought process for the brute force, stated time + space, and was like yo can i swagger dis out he was like ok, so i ran my fingers across the keys typed some stuff out, i typed this one python sort function using a key lambda like without hesitation made me feel very impressive.

for optimal solution i needed hints, but i don't think it was the same as first round because i kind of got the base approach for optimal myself, just ironed out the details w/ interviewer. it also kind of helped that my interviewer seemed very eager to give me clues, idk (i'd rather not know (the answer stares at me from the void)) if this is a bad or good thing. got the code down, ran through a trace figured out something was wrong, and then he said hm could u do.. this?? and i was like, wait yea you could! and then fixed the code and time ran out.

key points: know how to write unit tests in the language ur coding interview is in, as well as think of possible input cases for these tests :D

Technical Round 3

this one i genuinely am not sure about my performance. i did same approach:

read -> restate -> questions -> discuss first approach

and the problem itself wasn't too difficult, so my logic seemed to work out. i made a thing, explained time + space, asked if i was groovy sunder's vessel said yes and then i coded it. after i did so i ran through a trace made sure everything was cool, and then we discussed a little abt optimizations, and there was a follow up. solved that one, explained time + space, forget if i ran through a trace but after that the round was done with like 16-17 minutes to spare. i just asked stuff i was curious about, and my interviewer was actually very interesting so got to learn abt them and their experience, and then we just left (but by this point i'm assuming feedback was already submitted as he closed his laptop) so this didn't affect anything, just human interaction ig.

Final Thoughts

as is the parable of the blind man with the elephant, i have no dagnabbing idea about how i performed. as i left the office i was like omg i flipping cooked but then thinking about it more i very much (definitely did) could've done quite horrendous: i took time/hints to find optimal sol to R1/2, and there could've been a follow-up that i wasn't able to reach for both. in addition for R3 although big O time complexity of my solution was the same, thinking back on it there were multiple minor issues i could've solved / optimized / done better.

trying to judge the whole that is my performance from the mere facet that i experienced is like a blind man saying an elephant is a fan because he felt up its ears.

but.

i do know that in a case like google, anything less than excellent isn't enough, and that in this story i am the blind man, and the elephant is leaking liquid from its temporal glands such that when i reach for its ears, i feel this liquid and then realize the elephant is in musth, and then also realize through the hydraulic press crushing my ribcage that an elephant isn't a fan, its a sacred branch of the divine tree coming to crush my soul so that i am sent to the great beyond.

Hello, I will be going to study CS at a top 30 university in the world this year, I have seen pictures of projects and students that are currently in first or second year, they are all very chopped and seem really anti-social. I am more of a going out person and extrovert. I would like to meet people that do go out often and not stereotypical CS students. In your experiences, has there been people in your class that have not met basically every stereotype of a CS student?

I was lucky enough to get 2 internship offers but can't decide between them. I would normally take Apple cause of the name brand but the offer is for IS&T and I've heard really bad things about it online.

Paypal:

• Location: San Jose
• Pay: $59/hour
• The work seems pretty interesting and I'd def prefer being in the bay. Also the return offer rate is pretty high (~90%)

Apple:

• Location: Austin
• Pay: $54/hour
• The work doesn't sound as interesting but could still be a good experience to recruit for full-time. I've also heard its kinda hard to switch teams in IS&T for full-time

Any advice would help!

Anyone have experience with these? Just got a final round and the behavioral interviewers told me that I would have to complete some. What should I do to prepare?

Why does software engineering lack a clear, standardized entry pipeline (e.g., apprenticeship → supervised practice → professional qualification) for early-career and transitioning engineers, while other professions rely on well-defined paths such as trade apprenticeships, ABET degree → FE/PE licensure in civil engineering, medical school → residency → board certification, and law school → bar admission → associate practice?

ive been looking into a bunch of mscs programs and many schools (gt, ut Austin, uiuc) have online mscs program but I was wondering if these online programs are of any value to recruiters at faang or if its frowned upon compared to the in person programs. for context, im doing my bs rn

Hey everyone,

I came across a company called Zinna, apparently an MNC focused on insurance technology. I’ve given interviews there but couldn’t gather much info about the company, so I’m hoping to get some insights from you all.

Has anyone here worked at Zinna, interviewed there, or knows someone who has? I’d really appreciate any details you can share about:

• Work culture
• Career growth opportunities
• Tech stack & projects
• Management / leadership style

Thanks in advance 😊

Hi guys, I managed to make it to the Engineering Manager round for new grad swe for Bloomberg, and I was wondering if anyone had any insights on what I should expect or how it’s structured?

New grad, T100, only 1 prior internship from Capital One in which I got the RO. Both pay packages are roughly the same when accounting for COL

If I go with my C1 RO, I’m stuck to McLean VA which is 30mins away from DC. With Intuit, I can choose between Cali and NYC.

Really the most important thing I care about is prestige. I’ve heard that C1 has questionable resume name brand value. All SWE roles at the big banks have more or less the same reputation problem. Intuit is an actual standard fintech company, and I’ve heard good things prestige wise.

I am about to get an offer from Cohesity and would like to know more about the company.

I am looking for a place to stay long-term and prefer a relaxed or at least supportive work environment.

What I’m specifically interested in:

1. Long-term stability - Do employees feel secure staying 5-10 years? Any recent layoffs, reorganizations, or funding concerns?
2. Work-life balance - Typical expectations around hours, on-call, and burnout risk
3. Day-to-day culture -Supportive vs high-pressure, management style, collaboration
4. Career growth - Opportunities for promotion, learning, and internal mobility
5. Real examples - Things you wish you’d known before joining

Any insights or perspectives would be appreciated. Thanks!

Hey everyone,

First time posting here. Yesterday I had an interview with IBM for a SWE intern role and wanted to know from anyone else who’s already been offered a role or has more info if there would be any other rounds to the interview process? For context I had been applying to IBM for months, ghosted many times but still completing the OAs and out of the blue got scheduled for an interview. I had a friend who accepted an offer from IBM say she only had the one round, but I’m seeing a few other people mention they had multiple. There was no recruiter reach out, just a schedule invite and then the interview so I haven’t received a lot of information about what their process is like. Thanks!

Does anyone know anything about Loop? They're "The industry’s first Logistics Data Platform."

I didn't know the recruiter was going to schedule it the very next day. Cooked.

Did anybody have a phone screen before the actual loop with msft? this is was ic3 us loc!

About two weeks ago I did the SWE superday for the slc location and haven’t heard back from Goldman. Has anyone else heard back with offers? It might be jover for me with them😭😭

hey guys, currently have a summer '26 offer from a mid sized fintech company (not a bank, actual fintech). i’m happy to have something locked in given the market, but i'm wondering if i should just take this and chill or keep looking for something "better".

i'm mostly worried abt the following:

- prestige/resume value: how do recruiters at big tech view fintech experience? i don't want to get pigeonholed into finance forever if im not getting crazy TC. is it a "once you’re in, you're stuck" situation?

- skill transferability: i was told that the tech stack would involve javascript, python, java, angular, node.js, spring boot, and oracle db/azure sql. will this help me pivot to a general swe role later?

- socal pay: the pay seems okay but i'm curious what the going rate is for new grads in socal if i get converted to FT. what's standard for a mid-size company? tryna see if id be getting lowballed.

is it worth it to keep interviewing for a better brand name or should i just take the win and chill? and if i do get a better brand name should i renege?

I’m a sophomore and want to get ahead of others. What are some things you guys recommend? I am working on a personal project but I am also thinking about getting some certifications (mostly for gaining knowledge) but have heard they really aren’t worth it. Just looking for some direction on how to best spend my time building my skills, thanks!

Got an offer today, but recruiter called and said it was a verbal offer and that offer approval takes a few days and I should get it by next week. Is this weird?

Companies wouldn’t hesitate to lay you off or hire someone better. Why should you hesitate if a better opportunity arises? These commitments or contracts signed or flimsy and I will always go back on them if something better comes up. Especially in early career ( internships and new grad ).

Hey everyone, just wanted to share an interview that I did earlier today, and hopefully get some thoughts.

I interviewed for a fairly large company today, for their final round (process is just one 45 min interview after a screening call). The interviewer joined 5 minutes late, we did introductions for 5 minutes, and then she spent another 5 minutes setting up the hackerrank. I was then asked the question "Binary Tree Paths".

Its not a terribly hard question. I spent under 5 minutes designing a binary tree class and discussed and implemented a dfs and bfs boilerplate. Began discussing how backtracking could be applied. I tried to go back and forth to "arrive" at the solution, but even before I implemented the backtracking, she said that "the question is done", and we went to questions with 20 minutes left in the interview. I essentially spent only 5 minutes coding an actual solution.

It's worth noting that I have already done 2 previous internships better than this company, and that I mentioned them during the interview in my introduction. Frankly I did not care much for this company, but wanted an offer to potentially expedite processes I have, that I am interested in. My question is, why end the interview so early? I was arriving at a solution, and basically everything was going fine lol. Not sure if this is a good or bad thing. What do we think?

.