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A CS student’s job hunt in 2026: an honest playbook

Published 2026-05-20, Updated 2026-05-31

It’s harder to get hired as a junior engineer now than ever. There is lower demand (for juniors, but not necessarily for senior engineers) and more supply (i.e. competition). Cite: LinkedIn Jan 2026:

Record Computer Science (CS) colliding with a cooling hiring for entry-level software engineers (SWE).

The environment is harsher, so you have to be more intentional and work harder than your seniors did. Realistically, expect 100+ applications, lots of rejection, and a search that takes months. This is the norm, not a sign you’re doing it wrong. With that in mind, here is an updated list of advice I would give to myself if I were a CS junior today.

EDIT (5/31/2026): 80000hours.org has this awesome in-depth guide. I especially love this section on motivation:

Your first job search may be one of the hardest things you’ve ever done. You may have never been rejected 30 times in a row before. It can involve months of work. And you may have to do most of it alone. It can make online dating look easy. This means you’ll need to throw every motivational technique out there at it. The most useful is to pair up with someone else who’s job hunting.

Commit to daily habits

If you take away one thing from this post, take 5 minutes to do the following. (1) Schedule two recurring 30-minute blocks on your calendar every day — one for applying, one for upskilling. (2) Create 2 Google Docs to track action items for each session. For now, each doc should have only these two items: (1) fill in the list with items from this post (2) reorder by priority based on where you’re at and which jobs you’re targeting.

Prereqs

These are minimum requirements that you want to clear ASAP before you start applying.

Class: Data structure, runtimes. Take a junior-level data structures (UW CSE 332 Edmonds CS 143) class ASAP. I heard this recommendation a decade ago as a junior, and after a decade in industry, I still stand by it. If you don’t have a clear understanding of priority queues, hashing, and big-O runtimes, you will most likely fail technical interviews — and even if you pass, your teammates will be frustrated that they can’t have meaningful conversations with you about runtime performance and data manipulation.
Project: At least 1. Have at least 1 project, either personal or from a capstone class. Make sure it has a GitHub page that explains why the project is useful (a video works well — see example) and a link showing your design thinking (example).
Resume. Have a LinkedIn profile and a document-form resume. If you have time, also build a personal website.

Get interviews

Start ASAP. Start applying for internships and full-time opportunities as soon as you’ve finished your data structures class. Use multiple job search sites, look at your college’s career programs, and figure out when the interview cycles start (example).
Personalize your resume. Before you apply, tailor each resume to the job, if possible. AI makes this easy. E.g. for an embedded engineering role: (1) reorder your projects so the embedded capstone is at the top, and (2) update the profile summary so the hiring manager sees you as a great fit. A friend of mine wrote an AI-based script to automate this :D — something like: “Given this job description and my resume, can you output a personalized resume? Here are some personalization examples: …”.
Reach out actively. Send personalized cold emails to real people in addition to the hiring portal. If a company you’re really interested in doesn’t have a hiring portal, reach out anyway. See example.
Leverage connections. Is there anyone in your network willing to vouch for you? E.g. when I was a wee baby in community college, fresh out of my second CS course, I asked my professor if there was any collaboration opportunity I could take. He was kind enough to hook me up with a research project at another university.
Increase breadth. A tiered approach works well: for companies you really want, do the costlier tips like resume personalization; for the rest, just mass apply. If you’re not getting any bites for SWE positions, apply to adjacent roles like QA, support eng, devrel, TPM, IT, contracting, or smaller no-name startups. One success story: a friend of mine got a support eng role at a startup and was also offered SWE projects with the potential to switch to a SWE role.

Build skills in parallel

Budget your time: Level up v.s. apply. Figure out the right balance between time spent finding new opportunities and time spent upskilling. I personally would cap applications at 30 minutes per day, at the end of the day when my brain is tired. The rest is reserved for upskilling, unless I feel my skill set is already strong enough relative to peers who got jobs.
Show excellence, and network. Be excellent to the people in your program — professors and classmates. Do well in your classes. Show up to office hours with thoughtful questions. Find excellent classmates and do projects with them. Are you the kind of person your professors and classmates would be delighted to help succeed (e.g. referrals, mock interviews, sharing interview experience)?
Research the interview meta. For the companies you’re interested in, are they still asking leetcode questions? Do they allow AI? Ask your classmates who recently interviewed. Some sites like LeetCode (premium-only, but some lists are public) also show recent questions asked per company.
Research the work meta. What skills are now needed on the job at the junior level? Ask your seniors who recently got a job. E.g. Guha et al., 2025 notes that “being able to rapidly read and understand code is more important than ever.” Take courses and build side projects that exercise these skills.
Learn. Learn those skills either by taking courses (e.g. an algorithms course if leetcode-like questions will be asked; software engineering group project capstones never hurt) or by self-study.
Personal projects. Build more personal projects that tick the keywords your ideal jobs are likely to look for. Interested in mobile development? Look at the job postings and see which popular framework to use.
Personal list: AI, cloud infra, system design. This one is more personal. When I was in charge of hiring at a startup, my main qualm with junior engineers was that I couldn’t validate how effective they’d be as a colleague — they lacked the vocabulary to even talk about the practical building blocks of system design. I’d ask questions like “Where would you host this code?” and get uncertain answers like “Oh, idk, a jupyter notebook?”. Most university courses unfortunately don’t touch on the practical technologies real engineers use day-to-day, so you would have to learn these on your own time. To fill the gap, find walkthroughs you can follow along with (example) and build personal projects that exercise these concepts. As of May 2026, I would recommend that you at least know what these are for and be able to converse about them: [docker, terraform, LLM API, S3, EC2, dynamodb].

Pass the interview

Understand the format. Ask the recruiter what the interviews are like, and whether AI is allowed. E.g. from a friend I coached: “Per your advice, the day before I asked if I was allowed to use AI/the internet during the interview, and they said no. In the interview, the interviewer said 90% of candidates have been using AI and they would stop the call if I was caught, so I felt good knowing that ahead of time.”
Beyond coding interviews. Check if there are interviews beyond just coding rounds — usually system or behavioral rounds. Prepare for these too, including behavioral. Use AI to brainstorm a list of questions, answer each one, then add trigger keywords for each answer so you can recall your answer outline on demand.
Practice with humans. Ask your friends or mentors for a mock interview.
Practice with AI. Use LLMs to craft interview questions and practice against them. An example prompt: “Given this job posting […] and the emails from the recruiter […], what questions are they likely to ask?” I had great luck with this approach – I helped a friend pass a data analyst interview by giving a mock interview using this method. She said: “I wanted to let you know that the coding interview on Tuesday went well! The coding part was close to the practice you set up with tables, and I was asked to explain how I would achieve certain objectives in the Google Doc with SQL and Python. I felt super prepared based on the examples we worked through! “ Then a week later, a celebratory email: “I wanted to let you both know that I got this job!! I accepted the offer on Tuesday :)”

Hoo boy. That’s a lot of information. Now go back to the earlier section on “Commit to daily habits” and start executing!

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