You’ll find these tips most applicable if you study Computer Science and are seeking a software engineer internship.

• The career fair layout is like a spiral that ends up on the floor of the stadium. The larger booths tend to be on the floor, so they tend to be owned by larger companies who choose to afford it, sometimes popular ones. My tactic was to arrive early and start at the center (after some “practice”, see next) then work my way out of the spiral.
• “Practice” on the companies you don’t really want to work for, but who knows, you might not get offers from anyone you originally wanted to work for and have to settle, but you might end up liking it a lot and learn a ton anyway.

• The larger “sexier” companies often have the lowest ROI during the career fair. (“HI NICE to meEt you apply online”) If you really want to work at BIG 4 get a referral from the millions of alumni that work there. Meanwhile some of the smaller companies that do just as if not more interesting work may give you an interview just based on your interactions at career fair.
• I’ve actually heard a company say that they were turned off by students that said they wanted to work on machine learning/data science. Even if you’re actually capable of typing import tensorflow as tf, tread carefully because a lot of students did this in 2015 and the claim of this skill became trivialized. And if you’re talking to a 3-person to-do list startup, what data do you think they could they possibly use ML on? Obviously don’t falsify your preferences if you’re adamant about working on ML/DS.
• Try to do even a minimum amount of research into what a company does before talking to them, it makes you stand out. Even better is to prepare questions specific to a company’s product or business, as it signals that A) you did your research, and B) you care about more than just the tech. It will be something they remember you by. Also nice question-types if you can hold down a conversation about it: “How is your build infra? How often are engineers expected to write tests?”
• Getting asked technical questions is within the realm of possibility! Bloomberg might do this. It’s typically a low bar though, something about resolving hash map collisions.
• Look your best, unfortunately how you look is a big part of the impression you make. Business casual leaning towards casual. Well-fitting and clean, wrinkle-free clothes. The Internet is your friend if you want to learn how to dress well.

• Smell good, I know it’s Texas and it’s in September but if you smell bad nobody is going to want to talk to you much less help you get an internship/job. Breath mints and deodorant are your friends.
• Don’t be desperate, people will think something is wrong with you and that recommending you is a bad idea. Make sure you don’t seem cocky but competent.
• Have a 30-sec elevator pitch ready, this will be your first impression. Make sure it stands out. “i am [REDACTED] and I’ll be a rising junior this summer. I did full-stack dev in Python and JavaScript for a B2B SaaS company in Austin last summer, and most recently won an award at a hackathon in San Francisco for a graph visualization of 18000 text messages analyzed for sentiment. My interests mostly lie in backend API design and data modeling, but I’ve done some frontend work before.”
• Countersignal. If you’re fantastic, wear athletic clothing. They’ll think you’re an elite coder. (joke)
• If your friend referred you before career fair, MENTION this to the recruiter and they will PRIORITIZE you for an ON-CAMPUS INTERVIEW. (“Hey you know my friend `${props.name}`? They referred me the other day, was wondering if y’all got that??”)
• On a similar note, I’ve seen situations where a friend applied online and got a referral shortly after, but got rejected online before the referral was processed. Unless it’s part of the protocol, don’t apply online if you’re getting a referral.
• Follow up with your recruiters and email addresses IN THE NEXT 2–3 DAYS. Preferably night of. Make a meaningful subject line. “The one who made a Kanye chatbot” (one I actually used was “UT Career Fair follow-up (I love Python!)” though that probably isn’t very unique anymore)
• If you know the recruiters’ names, look them up on LinkedIn and find out something y’all might have in common. Be tactful not creepy.
• Apparently shit like this works too? But try not to bring the average interestingness of the CS/eng crowd down to the point that recruiters find smOOth, bright, THICC paper interesting.
• Don’t assume women are recruiters and not engineers, and also have a good eye for who might be able to advocate for your resume — if you’re a bit awkward but you’re able to talk about something brilliant you’ve done, an engineer may be able to see through the awkwardness— I’ve been in situations where I believed that if I hadn’t talked to this one person, my resume would’ve ended up in the discard pile.
• This touches more on choosing an offer and is out of the intended scope, but location is beneficial in some aspects for “mobility” or “optionality”. If I had picked an offer with a more established company outside of the Bay Area, I would not have had as much exposure to networking events exclusively for interns in the area…such as Techfair (which I didn’t attend, but other friends did) or random unicorn startup networking nights.
• Embrace serendipity. There was a long-ass line for this company and I didn’t realize that there was a line (gap for the hallway) when I started giving my elevator pitch to the engineer — who ended up being my mentor at that company the following summer. I had the choice to walk away embarrassed, or go to the back of the line, and I had a great summer from the latter.
• Don’t tie your self-worth to how many interviews you get. Much of career fair is signaling and many people are good at that but can’t actually code.
• If you followed all of this advice and got a lot of interviews, a spreadsheet like below might be worth the investment (names redacted). Funnelize your career and find out this season’s conversion rate, iterate and figure out the best strategy for next season using machine learning algos.

• If this helped, donate 5% of your intern salary to the Rainier and Prad fund and 10% of it to the Against Malaria Foundation, or just follow @rainieratx and @Prad_PL on Twitter :)

Made with ❤

A. Find a startup on AngelList that would be willing and able to provide a temporary work visa. This will usually involve a mixture of desperation from the startup’s side (typically if there’s a lack of specific domain expertise in that geographic region) and demonstrating that you’re highly skilled. This is harder for non-domain specific work like generic app development or social media marketing, since those things can be somewhat easily executed remotely and there’s more likely to be local talent in those areas.

B. Work at a big company that has international offices. This will usually involve interning there at least once already to show that you’re worth the investment — it can be their play to convince you to accept a return offer — OR having an insane amount of leverage from strong competing offers that may also be located abroad. Two specific examples of this that I’ve heard about: Someone who interned at a “big 5" once, got a return internship offer, then asked for their London office. Another person that interned at a large company, got a return offer in their London office, then used this to negotiate an offer with a competing company to work in their Zurich office.

Which camp did I fall into? Well, I interned at a startup, but didn’t get the opportunity through path A. Instead, I met the traveling CEO of the company at a hackathon by pure chance when they were looking for a team to join. At the beginning of the hackathon, there was a team formation session where every project lead pitched their idea to the participants. I pitched something really dumb (it was the SXSW Music Hackathon in 2016). I think it was an app that “recommends indie artists for you to listen to while you’re in a Lyft on your way to a concert, and lets you donate cryptocurrencies to them using blockchain technology.” A few hundred lines of backend API code in Python and 36 hours later, I received this email:

I couldn’t turn this down! I mean, it’s machine learning for CRISPR genome editing in London... But I didn’t want to renege on an offer I already had for that summer, so I requested for it to be pushed to the fall. I ended up having more time and two amazing, completely different experiences in the Bay Area and London.

So for me, luck was a huge factor. Lessons learned from this?

Getting internships from networking at hackathons is a real thing. But I wouldn’t place it under a third, “path C” for this question because the probability of getting an internship from a hackathon times the prior of that opportunity being abroad is already pretty low.

Working hard and showing up can maximize your “opportunity surface area”! I wasn’t looking for another internship at the time, but I applied skills that I’d worked hard on regardless of that. To be honest, I went to that hackathon mostly due to peer pressure from my friends, and I’m glad I showed up that weekend.

You should try interning or studying abroad. Few experiences in your life will change the way you think or expand your life’s perspective as much as spending some time living abroad. If you’re working, you’ll get paid to do it, and if you’re studying, you’ll be surrounded by a lot of peers.

Cheers!

The case-based approach

We intended for the learning approach to be similar to that of a case competition or hackathon throughout the semester. These cases were designed with the help of alumni and external advisors to incorporate aspects of the product development lifecycle. For each case, we provided some background, an intentionally vague problem statement, and hints at possible solutions. We outlined a list of potential deliverables before the end of the semester, depending on the skill level and interests of the team:

• Front-end mockup inspired by findings from user experience research (potentially accompanied by its implementation in HTML and CSS)
• Back-end with the solution’s core features with mock data (if applicable)
• Presentation slides (similar to a pitch deck) along with a business plan — if it’s a B2B SaaS, develop a pricing model and projections

They were not expected to complete a fully-functional solution — instead we were aiming for a “proof-of-concept” for what they had come up with. What they came up with overall was pretty impressive! All of their slide decks contained some indication of existing pain points, a SWOT analysis, NABC outline, and many of the teams wrote actual working code!

Each Build Team worked on one of four cases throughout the semester. Note that we did not actually partner with the following companies — they were used as an education tool for developing proof-of-concepts to propose improvements to real companies.

Here they are:

Consumer Analytics — Know how Netflix recommends shows to you based on what other people like you have watched? That’s called collaborative filtering, and that’s what one of teams worked on using the Yelp data set. Using the history of restaurants you’ve been to, it could recommend other restaurants that people like you also enjoyed. Another team designed a creative way to consolidate “stamp card” reward eligibility for frequent patrons at restaurants, in an all-in-one stamp card app. One team focused on redesigning Yelp’s review process to prioritize ease of access and usability. Finally, there was a team focused on an algorithm to identify “trendsetters”, i.e., “early adopters” that review new restaurants with a sentiment that ends up being consistent with the average once more reviews pile in. They even took on the potential issue of “elite” Yelp reviewers being promoted to review newly-launched restaurants well by designing an anonymity feature!

Retail Technology — Wi-Fi beacons in department stores that help shoppers using an app to locate items, and assist salespeople by updating the store’s inventory in real-time! An app that randomizes outfits from JCPenney’s selection to fit your preferences using machine learning, and gives you discounts! A “flash sale” countdown feature added onto JCPenney’s site to take advantage of millennial impulse-buying, “awaken competitive spirit in people; the urgency and excitement clouds judgement” (verbatim from their slides). We were impressed by the creative ideas and research that came out of this group.

Social Media Monetization — Lots of companies have customer support accounts on Twitter (e.g., AppleSupport), which people tweet at and receive actual help in their replies. So, one team designed a customer support-oriented B2B product that centralizes customer support tweets for company employees to easily use and delegate response tasks. One team performed user experience research on a newspaper-like tab for Twitter. Another team developed a marketplace tab for users to buy and sell items, kind of like Facebook Marketplace. This team had a fully-functional backend written in Java, utilizing design patterns and everything!

Market Analytics — This case admittedly had the broadest scope, which led to some initial confusion, but the students ended up working on the ideas they were most passionate about. While the two teams assigned to this case ultimately settled on using data collected using the Twitter API, each team applied their data in very different ways. One created a Bitcoin price predictor based on real-time sentiment analysis of tweets, while the other used natural language processing techniques to gauge historical public opinion on Brexit against the British Pound.

Engineering an organization

Here’s a sneak peek at our internal processes.

Lead Recruitment — Each group of teams working on a case had four Build Team leads, so we found 16 leads, who are formally considered officers of Convergent. These were usually upperclassmen whom we interviewed at the beginning of the semester and selected for using a number of factors:

• internship experience/experience in other organizations
• intuition for business and product development (a typical conversation brought up with every candidate, regardless of major, could have been to pick apart Amazon’s profits and losses by product, or compare Snap and Instagram’s user growth, and so on)
• approachability and passion for helping others

Approachability is a crucial yet underrated trait, especially as it comes to picking leaders that students will be looking up to and asking for help.

Sprints and Case Group Meetings — Build teams working on the same case met with the leads weekly and operated in one-week sprints, pretty much in the “agile” tradition. They were encouraged to use Slack for communication and Kanban boards on Trello to make it feel like working at a startup. This is how a typical Case Group (4 leads, ~25 students) meeting might have been structured:

• 0:00–5:00: Let latecomers trickle in, highs and lows, small talk.
• 5:00–25:00: Industry topic discussion — leads educate themselves prior to the meeting and bring up important topics that may or may not be related to their case, such as “What is the ‘cloud’, exactly?” or “How do machines ‘learn’?” We have standards for how these discussions are guided, to encourage a Socratic teaching strategy.
• 25:00–40:00: Mix of a weekly standup or sprint retrospective (occasionally a presentation from each Build Team to the whole Case Group), and plan for next week.
• 40:00–60:00: Q&A, work on cases, individual Build Teams coordinate to work on the case outside of the Case Group meeting.

After each meeting, the leads were instructed to fill out a document that contained four points: Attendance (how many students showed up to your meeting?), Activity (what did y’all do?), Engagement (how engaged were your students?), and Assignment (what work did y’all assign to them?). The goal of specifically addressing these four points was to provide transparency to the officers and document each others’ strategies.

Officer Meetings — One meeting per week for the director and leads to meet and pool together ideas and feedback for next week. These are efficient and fun; we made sure that these were meetings that officers would be looking forward to, instead of dreading!

Reflections and conclusion

To be honest, we started out with some lack of standardized processes for certain systems (e.g., collecting attendance, marketing) and inaccurate assumptions based off our experience with a not-so-similar sample of orgs. That means our first semester had a lot of trial and error. We ended up with data in the form of feedback, rough estimates of student engagement, and used these points to quickly iterate on and make the Build Team experience better on a weekly basis.

We ended up running Convergent like one might run a startup, which was pretty fun. Our officers put on many hats — in selling the organization to others, devising retention strategies for our students, or explaining how a web server works. Indeed, that’s because a lot of our structures, values, and internal processes stem from our collective experience at firms like these:

We hope that the Build Team experience was beneficial for our members — both as something to demonstrate to potential employers as well as an opportunity for new friendships, maybe future business partnerships.

What’s next?

This spring, we look forward to delivering an even better Build Team experience using what we learned from our first semester!

First, we’re adding a few more questions to our interest form to gauge applicants’ existing skills and match them to leads who can best accommodate their growth. Next off, we’re switching the four cases we had last fall with four completely new cases, including one focused on applications of blockchain technology! Finally, because the number of teams working on each case is roughly the same number of leads on each case, we will make an effort to fully integrate one lead onto each team. This provides the chance for easier and welcoming collaboration between the leads and the students when it comes to implementation-specific issues.

A student from this semester approached one of our leads and said that it was with the help of their case that they landed their first internship offer. Another started their own company, which was recently accepted into UT’s highly-competitive Longhorn Startup Lab. That is what Convergent is enabling.

Be on the lookout for posts on this Medium, by students of the build teams!

— rainieratx

Build Teams at Texas Convergent was originally published in Texas Convergent on Medium, where people are continuing the conversation by highlighting and responding to this story.

None of these recommendations are meant to be taken as me agreeing with them (okay, maybe some of them 😜). In fact, some of these authors’ opinions widely contradict with each other, and your gut reactions to them might be negative. But they’ve made me think and might make you think, too.

Most of these articles apply to a person who wants to pursue a career in the tech industry, but the articles under the Learning and Misc headers should apply to just about anyone. Only the articles under the Software engineering header are remotely technical (though even non-technical people may find Jeff Meyerson’s perspective as an engineer, intriguing).

For each one, I’ve supplied their title, author, and a representative sample of the author’s argument.

Career advice

Advice for ambitious 19 year olds (Sam Altman)

“No matter what you choose, build stuff and be around smart people. “Stuff” can be a lot of different things — open source projects outside of class, a startup, a new sales process at a company you work at — but, obviously, sitting around talking with your friends about how you guys really should build a website together does not count…”

PhDs for Entrepreneurs (Dan Zhang)

“However, a game-changing research paper could change your life far more than the average successful startup product launch. Aside from the resulting fame, many such papers have been spun off into wildly successful companies. The most obvious example is the publication of the PageRank algorithm, which led to a series of events resulting in…”

From Student Side Project to Startup (Paul Dornier — Y Combinator Blog)

“…it’s here that student founders actually have a unique advantage over other founders; you’re already surrounded by thousands of supportive and helpful beta testers (your classmates and school alumni)…”

Max Levchin of Affirm: Seeking the Endurance Athletes of Business (Max Levchin — Interview with New York Times)

“So I always tell people go to a start-up while you’re young. You might believe that going to a more established company to build up $100,000 in savings is your ticket to go take a big risk. It really isn’t. It just slows you down and makes you feel like you need to get to $200,000…”

This Is Why Your Startup Will Fail (Brian Scordato — Fast Company)

“The startup idea you pursue should be one you’ve been unconsciously preparing for your whole life. It should be about your strengths, not just a gap you see in a market. What are you, uniquely, incredible at? What do you know that no one else does? The first thing you should ask yourself when you have an idea is, “Why am I the best person to start this?”

If you ever find yourself sitting in a coffeeshop, quietly pitching your startup idea while side-eyeing everyone else within earshot — stop…”

How technology is shaping our future: billions of self-employed makers and a few mega corporations (Pieter Levels)

“Think critically of your own job and skill set and judge how unique it is. Can it be automated easily? If your job is administrative, non-creative, repetitive, it’ll probably be gone soon. If you’re doing a lot of IRL stuff like sales, meetings, driving around to visit companies and then talk buzzword bingo bullshit, then you’re probably out too.

So if you didn’t get angry and don’t feel entitled, but instead are “embracing” this change, what’s there to do?…”

Learning

Fixed vs. Growth: The Two Basic Mindsets That Shape Our Lives (review of Carol Dweck’s research, Maria Popova — Brain Pickings)

“In the fixed mindset, that process is scored by an internal monologue of constant judging and evaluation, using every piece of information as evidence either for or against such assessments as whether you’re a good person, whether your partner is selfish, or whether you are better than the person next to you. In a growth mindset, on the other hand, the internal monologue is not one of judgment but one of voracious appetite for learning, constantly seeking out the kind of input that you can metabolize into learning and constructive action.”

Strong Opinions weakly held (Paul Saffo)

“The point of forecasting is not to attempt illusory certainty, but to identify the full range of possible outcomes. Try as one might, when one looks into the future, there is no such thing as “complete” information, much less a “complete” forecast. As a consequence, I have found that the fastest way to an effective forecast is…”

52 Concepts To Add To Your Cognitive Toolkit (Peter McIntyre)

“21. Bikeshedding — substituting a hard and important problem for an easy and inconsequential one. When designing a nuclear plant, Parkinson observed that the committee dedicated a disproportionate amount of time to designing the bikeshed — which materials should it be made of…”

Straussian Reading (Kevin Lacker)

“Why would someone write in this way, with hidden esoteric meaning, rather than just saying what they mean? In this example, your beloved feels that you can’t handle the raw truth. Fear of persecution is another common rationale for esoteric writing. Socrates was executed for his beliefs, so do you really think Plato would just write down everything he honestly believed?…”

Social issues

Women Are Over-Mentored (But Under-Sponsored) (Herminia Ibarra — Harvard Business Review)

“When we use the term sponsoring, we focus in on that one specific function of mentoring, which may or may not be a part of a relationship. And sponsoring really is a very targeted thing. It has to do with fighting to get somebody a promotion, mentioning their name in an appointments meeting, and making sure that the person that you’re sponsoring gets the next assignment, and gets visible and developmental assignments…

Biased by Design (Y-Vonne Hutchinson — MIT Technology Review)

“Biases can become embedded in a product during any period of the development process. If the people making the products happen to come from a group that rarely experiences discrimination, those people will have a harder time predicting how bias will manifest itself. As an example…”

Silent Technical Privilege (Philip Guo — Slate)

“Instead of facing implicit bias or stereotype threat, I had the privilege of implicit endorsement. For instance, whenever I attended technical meetings, people would assume that I knew what I was doing (regardless of whether I did or not) and treat me accordingly. If I stared at someone in silence and nodded as they were talking, they would assume that I understood, not that I was clueless. Nobody ever talked down to me, and I always got the benefit of the doubt in technical settings…”

Misc

WTF is a hackathon? (Dave Fontenot)

“A notable example of a hackathon “hack,” GroupMe is a group messaging app that was acquired by Skype for over $50 million. Other notable examples include the Facebook “Like” button and Facebook Chat which were both first demoed at internal company hackathons…”

The secrets to a truly restorative vacation (Alex Soojung-Kim Pang — TED article)

“Mastery experiences are engaging, interesting things that you do well. They’re often challenging, but this makes them mentally absorbing and all the more rewarding when they’re proficiently executed. (These also make your life more meaningful; psychologist Mihaly Csikszentmihalyi has found that people who seek out “flow” experiences in difficult but rewarding activities are happier and have more satisfying lives than people who pursue sybaritic pleasures.)…”

The End of the Internet Dream (Jennifer Stisa Granick — Black Hat 2015 Keynote)

“Twenty years from now,

• You won’t necessarily know anything about the decisions that affect your rights, like whether you get a loan, a job, or if a car runs over you. Things will get decided by data-crunching computer algorithms and no human will really be able to understand why.

• The Internet will become a lot more like TV and a lot less like the global conversation we envisioned 20 years ago.

• Rather than being overturned, existing power structures will be reinforced and replicated, and…”

Facebook and the Cost of Monopoly (Ben Thompson — Stratechery)

“The result of monopoly pricing is that consumer surplus is reduced and producer surplus is increased; the reason we care as a society, though, is the part in brown: that is deadweight loss. Some amount of demand that would be served by a competitive market is being ignored, which means there is no surplus of any kind being generated…”

Hacking DNA: The Story of CRISPR, Ken Thompson, and the Gene Drive (Geoff Ralston — Y Combinator Blog)

“The very nature of the human race is about to change. This change will be radical and rapid beyond anything in our species’ history. A chapter of our story just ended and the next chapter has begun…”

Software engineering

10 Philosophies for Engineers (Jeff Meyerson — Software Engineering Daily)

“There is a narrative of a programmer who is incapable of doing anything except programming. Some programmers talk about this with pride, saying things like “I am just an engineer, I don’t want to think about the business side of things, I don’t understand the business side of things”.

Engineers have been seduced by the industrialist’s perspective that we cannot lead ourselves, we cannot evaluate opportunity cost, and we don’t understand the market as a whole.

All of these are lies, and the world will be more efficient and utilitarian if engineers take control of their careers and start evaluating the options outside of their immediate, narrow context…”

What are the things required to become a hardcore programmer? (Edmond Lau — Quora answer)

“Say it takes you 12 seconds to search and navigate from a function call to where it’s defined. And suppose you have to do similar navigations 60 times per day. That’s 12 minutes per day spent navigating files. If you learn enough keyboard shortcuts in your text editor to navigate to a file in 2 seconds instead of 12, then over the course of a day, you’d save 10 minutes. Every year, you’d save an entire 40-hour work week…”

Machine learning is the new algorithms (Hal Daume — Natural Language Processing Blog)

“I would actually go further. Suppose you have a problem whose inputs are ill-specified (as they always are when dealing with data), and whose structure actually does look like a flow problem. There are two CS students trying to solve this problem. Akiko knows about machine learning but not flows; Bob knows about flows but not machine learning. Bob tries to massage his data by hand into the input to an optimal flow algorithm, and then solves it exactly. Akiko uses machine learning to get good edge weights and hacks together some greedy algorithm for flows, not even knowing it’s called a flow. Who’s solution works better? I would put almost any amount of money on…”

Who Y Combinator Companies Want (Triplebyte)

“In the meantime, programmers looking for jobs with YC companies may want focus more on product and be sure to mention experience outside of Java and C#…”

You Are Not A Commodity (Jeff Meyerson — Software Engineering Daily)

“The average case always looks good for strong engineer. If you are a good engineer, your downside risk is capped in most conceivable futures.

It is almost impossible to imagine a world in which engineers are not in high demand. In the hypothetical scenario where a good engineer is not employable, it is likely that something will have gone drastically wrong with the economy…”

Hey!

If you have any article suggestions, please comment below or find me on Twitter @rainieratx. I enjoy hearing different and well-argued perspectives.