the postings make no sense. half are SDR with a fancier hat. half want an actual software engineer. half are just RevOps that went through a rebrand.
before i spend six months skilling into a title that might get folded back into "sales ops" the second the hype cools: people who actually hold this title, what do you DO all day, and is it a career or a costume?
I applied through LinkedIn's careers page and heard back a few weeks later. From the initial recruiter outreach to the final decision, the entire process took around three to six weeks.
Communication was consistent throughout the process, and the recruiter kept me updated between each stage.
Recruiter Screen (15-30 min): Background, motivation, quick overview of the process. They share resources if you ask.
Technical Phone Screen (60 min): Live coding on HackerRank. One LC medium problem. The expectation was to explain logic clearly, not just run the code.
Onsite (5 rounds, 45–60 min each): Two coding rounds with one medium and one hard problem each (40 minutes total). Topics included graphs, heaps, arrays, linked lists, nested list weighted sum, and BFS shortest path.
One system design round (design a distributed rate limiter). Senior candidates may get LinkedIn-specific designs like a recommendation engine or social graph.
One behavioral round with the hiring manager focused on conflict, ownership, and ambiguity.
One project deep dive covering technical decisions, cross-functional collaboration, and communication with non-technical stakeholders.
Team Matching: Came after the loop. A separate conversation to align on which team fits.
Happy to answer questions below.
I recently completed the full interview process for Palantir's New Grad Software Engineer role, from the initial recruiter call through the hiring manager conversation and did receive an offer at the end.
Online Assessment (HackerRank): Three parts. One coding problem, one SQL query, and one REST API task. The API question had me use a REST endpoint to find TV shows produced in a specific period.
Only place I used InterviewCoder.
Virtual Onsite: Back-to-back rounds, coding and system design plus a debugging round. The debugging round gave me a pre-written codebase of over 250 lines with subtle logical bugs to find and fix. One of them was a double-counting issue in a HashMap.
Hiring Manager Chat: The whole call went to my biggest failures and what I learned from them.
Update (Talked with another applicant): His OA was 2 medium-easy LeetCode questions plus a REST API design, and his technical round was designing a round-robin style system with a hashmap and lists.
Happy to answer any questions below.
I graduated from a T20 this spring and I’ve got 2 different offers for the fall and I can’t make a decision.
Option 1: Meta, Software Engineer, San Francisco, $240k TC
Option 2: Top quant trading firm, Quant Trader, NYC, ~$420k expected compensation
I've been thinking about this for already a week and still don't know what to do.
I enjoy building software, AI is growing insanely fast, and being in SF seems like a great place if I eventually want to work at a startup or even start my own company (something I seriously plan on doing). All of this is making me want to choose Meta.
On the other side, for the quant job, the compensation is obviously much higher, and it's a field that's notoriously hard to break into. Over a longer period of time, I defo think I'm going to make more money in quant, even though I'm not passionate about it.
At the same time, I wonder how specialized the career is. If I spend 4-5 years as a quant trader, is it much harder to pivot into something else later? I also hear very mixed things about the lifestyle depending on the firm. Some traders have terrible living conditions.
Anything else I should know ? Looking for advice.
I had the UBS Sales and Trading summer analyst process this cycle. Superday is done and I am still waiting on the decision.
Networking: Two coffee chats before anything formal moved. Both were short calls with people at the bank.
Online Assessment: One skills assessment completed online, sent after my application went through. (Cluely is super useful here)
HireVue (recorded, no live interviewer): Timed video answers to a camera. Talking to a camera with nobody on the other end took me one question to get used to. I ran Cluely during it and finished every answer inside the timer.
Superday (3 interviews, 30 minutes each): Every interviewer was MD level or above. Almost everything was behavioral and fit, with a small number of light technical questions across the three interviews.
Update (compared notes with another applicant): Their assessment was split into four tests (behavioral, numerical, and analytical reasoning) and their HireVue had 5 questions, 1 minute to read each and 2 minutes to answer.
Sharing this because almost every superday post I read while prepping was for IB. I could not find one for Trading at UBS.
Drop your questions in the comments.
In 2026 buyers don't need vendors to educate them. AI tools handle category education, shortlist building, and feature comparisons before a human ever enters the conversation. The job has shifted from "generate demand" to "be the answer AI gives when demand already exists."
Are you reallocating budget from top of funnel content to bottom of funnel proof points, investing in review sites and third party citations, restructuring marketing teams around AI visibility instead of SEO, or rethinking what demand gen even means.
Looking for insights from CMOs, demand gen leaders, or anyone whose 2026 plan looks fundamentally different from 2024.