Forem: Major League Hacking (MLH)

Learning to Code: A Beginner's Guide for the AI Era

MLH Team — Fri, 17 Apr 2026 19:51:58 +0000

Most people remember the first time they opened a terminal. Maybe it was a Tuesday night, on a laptop at a kitchen table somewhere. The window was black. The cursor blinked. And the person sitting there was completely convinced that every other human on earth had been handed some secret manual they'd never seen.

The feeling that everyone else already knows this and I'm the only idiot in the room is the real reason many quit before they start. The work isn't too hard, and they're plenty smart enough. The trouble is that nobody ever hands them a map. They're just dropped into a forest and told the exit is somewhere to the north.

So here's a map. Something that experienced developers are only now starting to say out loud: The path in has changed over the last few years, and a lot of the old advice is out of date.

Don't Learn to Code. Learn to Build Things.

This is the single most important reframe for anyone starting out in 2026. The senior developers and design technologists who've been thinking hardest about the learning path keep landing on the same point: coding and making things are genuinely different activities, and conflating them is what sinks most beginners.

When we say "coding," we mean it in the traditional sense: memorizing syntax, learning paradigms, wrestling with package managers, and debugging a missing semicolon at 2am are just a few examples. It's what you do when migrating a database from one system to another. It's grunt work. No one wants to do grunt work. What people actually want when they "learn to code" is to make something – a website, a tool, a little app that solves an annoying problem in their own life. For Major League Hacking, it's often why someone comes to their first hackathon. Curiosity and a drive to try to build something new.

Those two goals used to be inseparable. If a person wanted to make a website, they had to spend three hours installing Homebrew, then three more installing Postgres, then three more configuring their development environment, before they ever typed a single line of the actual thing they wanted to build. Many beginners quit somewhere in those nine hours, convinced they just aren't cut out for it. The truth is they are – they're just being asked to build the entire toolbox before they get to put a hammer to a nail.

AI has broken that chain. The setup work, the boilerplate, the hunting through Stack Overflow for the right command – the stuff that used to eat the first two months of a beginner's life – can now be handled in a conversation. The craft hasn't disappeared. It's evolved.

The skill today is less about typing the code and more about knowing what to build, knowing whether what got built is any good, and knowing how to ask the right questions to get it there.

A New Coder Is Not Behind. They Are Normal.

More than one in three developers in Stack Overflow's 2024 survey had been coding for four years or less, and about a quarter of working professionals report just one to four years of experience. The industry is much younger than anyone would have you believe. And 82% of developers are learning with online resources, compared to 49% who learned in school – so the self-taught path is the real way in for most working developers today.

Software development as an industry is full of people who taught themselves the parts that mattered most, often while working other jobs, raising kids, or switching careers later in life. Changing careers at 35, parenting a toddler while learning, coming in from a liberal arts background — none of these are disadvantages. They're the everyday profiles of a new coder's future colleagues. And in the AI era, when the entire industry is learning a new way of working at the same time, everyone is a beginner at something.

What "First Language" Even Means Now

Often experienced developers get the same questions. One is "which language should I learn first?" Beginners can turn this into a weeks-long research project. They read comparison articles. They watch YouTube debates. They ask on Reddit and then argue with the replies. Months pass. They still haven't built anything.

Here's the updated answer, and it splits into two paths depending on where the learner wants to end up.

Path One: The Software Creator

Someone who wants to make a personal website, automate an annoying task, build a tool for their own life, or just prove to themselves they can make something new that works. This person should probably not spend months picking a language. They already have one: it's English or the language they grew up speaking. They can open a conversational AI tool – Claude, ChatGPT, Google's AI Studio – and describe what they want to build colloquially.

"Build me a portfolio website for a photographer."
"Write me a script that renames all the photos in this folder."

The AI can generate the code, explain what it did, and walk the learner through deploying it. This is vibe coding, and it has opened the door for an enormous number of people who would have quit during the Homebrew-install step five years ago. For this path, the "first language" question basically disappears. The first language is the one they have spoken all their life.

Path Two: The Aspiring Professional Developer

Someone who wants to work in software, whether that's engineering, DevOps, security, or any role where they'll need to audit, debug, and extend code written by other people (including AI). This person still benefits from learning a foundational language the old-fashioned way, and two of the most popular options are:

Python: The friendliest syntax in the business, reads almost like English, sitting at the top of the TIOBE Index since late 2022. Great for data, automation, and AI adjacency.
JavaScript: The language of the web, used by 62% of all developers in the past year, the most widely used language on the planet. Best for anything that lives in a browser.

For these learners, the advice is consistent: pick one language, don't agonize, and actually finish learning it. Your first language does something very important. It teaches you how to think like a programmer, and that thinking is what helps you audit output later. If an AI agent writes something subtly broken – an insecure database connection, a login form that leaks user data – the beginner who skipped foundations might ship the bug because they don't see it. The one who learned foundations has a real shot at catching it.

Many working developers recommend a hybrid approach to these two paths: Build the first thing with AI to catch the excitement and the momentum, then go back and ask the AI to teach instead of type. Prompts can look like:
"Explain why you structured it this way."
"What would happen if I changed this line?"
"Can I try writing this next part myself?"

Using AI as a coach rather than a grunt worker is a legitimate and increasingly common way to learn.

The Trap Called Tutorial Hell

Here's the part nobody warns new coders about. They'll find a great course. They'll finish it and feel incredible. Then they'll find another course, because the first one was so satisfying. Then another. They'll nod along to every video, understand every line the instructor types, and still freeze the moment they open a blank file on their own.

This is tutorial hell, and it can get almost anyone.

Why does it happen? Because watching someone code is like watching someone swim. A person can follow every stroke, memorize the breathing pattern, understand the biomechanics, and still sink the second they jump in. Think about watching the Olympics with your family. Every mistake elicits loud groans. Hopefully you're also turning and laughing together with the understanding that you can sit on a couch and critique, but landing that quadruple axel would be impossible without Ilia Malinin's years of experience, particular brand of confidence, and whatever brain chemicals send him launching himself into the air with blades on his feet.

Reading about a skill and performing a skill use different parts of the brain. The tutorials feel like progress because the brain is genuinely learning something – it's learning to recognize code. But recognizing code and writing code are miles apart. Nobody fully learns a new language on Duolingo. They learn it in conversation with native speakers, fumbling accents and verb conjugation, and generally looking a little bit silly. Then next time they pay at the bodega, they're a little bit better.

The rule experienced developers learn the hard way: finish one short tutorial, then immediately build something with it – even if that thing breaks.

What matters is that it's the learner's own, right away, even if it's tiny and ugly and barely works. Break it on purpose. Change things. Delete things. Google every error message that pops up. Or better, paste the error into an AI and ask what it means and why. That last part isn't cheating. 61% of all developers spend more than 30 minutes a day searching for answers, and most of them have been doing this for years. Looking things up is the skill. The professionals aren't the ones who have memorized everything – they're the ones who've gotten very good at finding the answer in the next 45 seconds.

AI as a Learning Tool

Now, 37% of developers use AI to help them learn, and it has quietly become one of the most powerful tutors ever invented. It's patient, available at 2am, willing to explain the same concept ten different ways until one of them clicks. The conversational nature has been a real breakthrough. A new coder can ask the questions they're embarrassed to ask in a bootcamp classroom, and get the answer in seconds instead of searching for it 500 different ways. That alone has reshaped what beginner education looks like. But the line between coach and crutch is real.

Asking AI to explain an error, unpack a concept, or walk through why code works the way it does is learning. Letting it write the project while the learner nods along is tutorial hell with a chatbot.

Build the Ugly Thing First

A new coder's first real project should be useful to exactly one person on earth: themselves. That's it. They are both the developer and the entire customer base. This is liberating, because it means nobody else has to like it, understand it, or ever see it.

Some projects that work beautifully for beginners:

A script that renames a folder full of photos from IMG_4823.jpg to something a person can actually search for.
A little webpage that shows today's weather in the learner's city.
A to-do list they actually use.
A tracker for how much they're spending on coffee this month.
A program that picks a random restaurant so nobody has to argue about dinner.
A tiny game like tic-tac-toe, hangman, rock-paper-scissors.
A portfolio site that replaces their ancient, and heavily templated, Squarespace.

These work because they share three traits. They're finishable. They solve something real for the creator. And when they break – and they will – the learner will actually care enough to fix them.

Please, resist the urge to build the next Instagram as a first project. A finished ugly thing beats an unfinished beautiful thing every single time.

When it's done, put it on GitHub, even if the code is embarrassing. GitHub is a source of real intimidation for many beginners, so it's worth demystifying: despite its reputation as a community space, it is fundamentally a version control tool. Think of it as Google Docs for code. It tracks every change the learner (or their AI assistant) makes, so nothing is ever truly lost. The community layer sits on top of that, not underneath it. A learner does not need to "earn" the right to be on GitHub. They just need a project and an account.

You can also bring it to DEV. Post about your project. Ask people for feedback. Read and respond to the comments. While MLH hackathons are incredible labs where you can build these first projects (or your 100th) with others over a few days, DEV is available 24/7. We think of ourselves as both the Lab (MLH), and the Library (DEV), where you can learn all the time.

The Disappearing Middle, and How to Not Be It

Here's the uncomfortable thing experienced developers might be quietly worrying about, and every new learner should know it too. AI has made the distance between absolute beginner and productive builder smaller than it has ever been. But it has also, at least for now, eroded the middle layer of expertise – the years a new developer used to spend wrestling with real problems on a team, learning what "good" actually looks like, absorbing the multi-point pre-flight checklist every senior engineer builds over a career.

That erosion is a problem, and the beginner who doesn't plan for it will get stuck.

A vibe-coded app can ship fast. It can also leak every user's data the moment it goes live – there are news stories about exactly this happening, more than once, in the last year. The beginner who built it with AI and never learned the underlying concepts may not know what went wrong, may not know how to find the thing that needs fixing, and may not know what questions to ask next time. The beginner who used AI as a coach – who built, then went back and examined the code, who asked why and what if – has a much better chance of being able to do these things.

The practical advice: build first, then audit. Ship the ugly thing, then open the code and ask the AI to walk through it line by line. Ask what it would do differently for a real product. Ask what security practices it skipped. Ask what a senior developer would change.

Over time, that accumulated questioning becomes the expertise the middle layer used to provide. The learners who will thrive in this era are the ones who use AI to build fast and then stay curious enough to learn what just happened.

How To Stick With It

There's a stretch, usually somewhere between month two and month six, where a new coder will feel dumber than they did the day they started. The tutorials got easy and then the real projects got hard, and everything in between feels like fog. This is not a sign the learner is failing. It's the sign they've left the shallow end.

Three things keep people in the chair through that stretch.

Shrink the goal: Thirty focused minutes a day will take a learner further than a six-hour Saturday binge, every single time. Consistency compounds. Burnout erases. Plenty of career switchers have made it through on forty-five minutes a day before work, for a year or more. It's a whole strategy.
Find a room: Other beginners are the difference between a hard month and a lonely one. An MLH hackathon, a Discord community, a DEV conversation, a local meetup, a coworker a few steps ahead, a friend who's also learning.
Keep a "done" list, not just a to-do list: Every week, write down what got built, learned, fixed, or figured out. "Got my first API call to work." "Finally understood what a for loop actually does." "Deployed my first site." On the weeks when everything feels stuck, the only thing that reliably works is scrolling back through that list and remembering the learner couldn't do any of it six months ago.

That list is proof. On the bad days, proof is the only thing that keeps a person typing.

The "Stateless" AI Era is a Massive Engineering Tax

MLH Team — Thu, 09 Apr 2026 15:42:13 +0000

We have spent a decade watching hackers grind through the same 48-hour cycle at MLH: build something brilliant, realize it has the memory of a goldfish, and then watch it break the second a user tries to do something complex. It is a mess. We keep pretending that stateless prompts are enough. They aren't.

We are tired of seeing great projects die because the "plumbing"—the session logic, the vector database, the state management—swallows 80% of the weekend. It is an engineering tax that student developers should not have to pay.

State Management and AI Memory for Developers

Did you know the "stateless" AI era is actually a huge bottleneck? Most builders are just stitching together fragments of history and hoping the LLM doesn't lose the thread. It is fragile. We have realized that to move past simple chat wrappers, you have to start with the memory. You should not have to rebuild your entire logic layer just because you want to switch from GPT-4o to Claude.

Integrating Backboard.io: The Stateful API for AI Applications

MLH is bringing Backboard.io into our ecosystem for the next 12 months. Backboard.io is a stateful API and the #1 ranked platform for AI memory on LoCoMo and LongMemEval. It acts as the persistent brain for your application, handling RAG workflows and tool calls through one integration — with support for 17,000+ models, built-in model routing, and bring-your-own-API-key so your entire existing stack becomes instantly stateful. This lets you scale a prototype into a production-ready agent without a complete rewrite of your foundation.

MLH x Backboard.io Partnership Benefits and Hackathon Access

We are making this a new standard for the MLH community. Starting April 10th at Global Hack Week, and across 50 events this year, we are giving every MLH builder free state management for life and the dev credits needed to actually ship.

We want to see what happens when you stop worrying about how your app will remember a user and start focusing on what the app actually does. If you are building at an MLH event this year, look for the "Best Use of Backboard.io" category.

Don't build another forgetful app. It is a waste of your time.

The Future of Software Has a Lot More Builders. They’re Going to Need a Home.

Jon Gottfried — Wed, 18 Feb 2026 14:00:30 +0000

I have some big news to share today: Major League Hacking has acquired DEV (dev.to), the developer community platform where millions of developers share technical knowledge, learn from each other, and grow together.

This is one of the biggest moves in MLH's 12-year history, and it's been a long time coming.

When @ben, @jess, and @peter started DEV almost a decade ago, they wanted to build something the internet didn't have enough of: a developer community built on generosity instead of gatekeeping. A place where people didn’t just ask questions, but actually helped each other. Over the years, DEV grew into a community of more than 3 million developers learning in public, sharing expert advice, and supporting one another in their journeys. It's become one of the most trusted spaces in tech, and anyone who's spent time on the platform knows why. The culture Ben, Jess, and Peter built is special.

At MLH, we've been building something complementary. Our developer programs (like the Hackathon League, the MLH Fellowship, and much more) are where developers learn by doing: picking up a tool, creating something real with it, breaking it, trying again. Between our in-person events and programs, MLH has served more than 1.5 million developers across nearly 100 countries.

If DEV is the library, MLH is the lab. One is where knowledge gets shared, organized, and preserved. The other is where you get your hands dirty and prove you can actually build. Together, MLH and DEV will reach approximately 5 million developers worldwide. These two community-first platforms have been orbiting each other for years, and we're thrilled to finally bring them together.

Why Now Is The Right Time To Combine MLH & DEV’s Powers

If you've spent years honing your craft as a developer, the value of your skills has grown exponentially over the last year. The judgment, taste, and architectural thinking you've built over a career are exactly the skills that matter most when AI can handle the boilerplate. The ceiling on what you can build has never been higher.

At the same time, the floor is rising. People who never would have written a line of code (or called themself a developer) are building real software with AI. More software creators, building more things, tackling more problems. The craft of software development isn't shrinking. It's expanding. The number of people who can build software is about to grow by an order of magnitude.

But when everyone can build, the skills that matter change. AI generates the bricks, but you still have to build the house. Taste, judgment, knowing what to build, which tools to trust, how to evaluate whether the output is actually good. Those skills don't come from a tutorial. They develop by building real things alongside other people.

And traditional institutions can't keep up. By the time a curriculum gets approved, the technology is already six months out of date. The old developer infrastructure was built for a world where people needed answers to technical questions. AI commoditized that. But the need for a developer community didn't disappear. It shifted. People don't need a database of answers anymore. They need somewhere to develop judgment and learn and build alongside others in a trusted space.

Together, MLH and DEV are building the core community infrastructure of the next era of technology.

What's Changing (And What's Not) For Both Communities

DEV will continue to be the community its members know and trust.
The culture that Ben, Jess, and Peter built is exactly why we wanted to do this, and we're committed to protecting it. We're here to invest in DEV and help it grow, not to change what makes it special.

Forem, the open source software that powers DEV, will also continue to evolve. This acquisition actually helps clarify the relationship between the organization and the software. We're working toward a structure that supports Forem's independence so it can serve the broader open source community on its own terms. More on that soon.

What IS changing is that MLH and DEV can now do things together that neither could do alone. The lab and the library, connected. You experiment with a new platform or tool at an MLH hackathon. You write about what you learned on DEV. That knowledge becomes searchable, permanent, useful to the next person.

Whether you're shipping your first project or you're a senior engineer picking up AI skills for the first time, you're welcome here. In the AI era, everyone is a beginner again at some point. MLH and DEV, together, are the place where that's not just okay, but encouraged.

Looking Forward To A Future of Software Creators

The world is about to have a lot more builders. They're going to need a place to learn, to experiment, to develop taste and judgment, and to find their aspirational figures and their collaborators. That's what MLH and DEV are building together.

Thank you and congratulations to Ben, Jess, Peter, and everyone who built DEV into the community it is today. And thank you to our MLH community for continuing to show up, build things, and support each other. You're the reason we get to do this work.

Happy Hacking,
Jon, Swift, and the entire MLH+DEV team

P.S. If you want to learn more about the acquisition or the future of MLH and DEV, my co-founder and our CEO Swift will be hosting an Ask Me Anything (AMA) with me (Jon) and the DEV founders (Peter, Ben, and Jess) on MLH’s Twitch channel on February 19 at 11:30am ET. We'll be answering questions from the community live. We hope to see you there!

Major League Hacking (MLH) Partners with Google Cloud’s Gemini to Foster AI-Native Engineering Education

Jenn Johnsey — Wed, 22 Oct 2025 14:48:00 +0000

We are thrilled to announce a new partnership between Major League Hacking (MLH) and Google Cloud. Over 3 years, we will be integrating Google Cloud’s latest Gemini models across our network of more than 4,000 community chapters with the goal of fostering a new generation of engineers who see AI as an integral part of their creative toolbox. We know competition is intensifying between AI platforms and that developer mindshare matters more than ever. Our own data shows Google Cloud’s Gemini models are experiencing the fastest growth in actual usage among next-gen developers, suggesting hands-on experience drives adoption more effectively than brand awareness alone.

How Major League Hacking & Google Cloud’s Gemini Will Work Together

We’re excited to start integrating Google’s Gemini models into MLH’s network of engineers, where 1 in 3 Computer Science grads each year are members of the MLH Community. It’s a central tenet of MLH that we understand real learning rarely takes place in a classroom–it takes place on the ground, in real situations where you learn to code and build, and create new things with the latest tools. Having these models at your fingertips will make MLH community members more equipped to build for the future.

While there is a three-phase plan in place to reach as many of our members as we can, the adoption of these AI models will take time to fully deploy. In Phase One, we will feature the models at 250+ MLH hackathons through dedicated prizes, workshops, and developer resources. Phase Two will amplify this initial utilization of the models with conferences and custom coding challenges so that more engineers are able to work directly with the models in real time. In Phase Three, Google’s Gemini models will be embedded into daily programming activities through local chapter meetups and hack nights.

It is our hope that we will be able to move through the phases of this plan in a way that gets Google’s Gemini models into the hands of engineers swiftly so that our communities can grow even as the technology becomes more and more advanced.

AI Model Exposure Builds Engineers of the Future

MLH is dedicated to creating educational opportunities for software creators and engineers at all levels of the learning experience. We know AI is already transforming software development, and early exposure to AI platforms during university years increasingly determines which tools developers will adopt throughout their careers.

We also know that diversity in engineering sectors matters. Exposing MLH’s diverse tech education community to these tools early is vital to ensuring the pipeline to professional software development remains as diverse as software users are. With nearly 50% of the MLH community identifying as non-male and two-thirds as people of color – significantly more diverse than traditional Computer Science programs – we are uniquely positioned to have an outsized effect on driving diversity in the industry. And our members span 93 countries, giving this partnership and Google’s Gemini models global reach among emerging developers.

MLH communities are passionate and driven to excellence. We can’t wait to see what our local chapters use Google Gemini models to build in the coming months and years. If you’re looking for a local instance of Major League Hacking, explore our upcoming hackathons and join us!

The post Major League Hacking (MLH) Partners with Google Cloud’s Gemini to Foster AI-Native Engineering Education appeared first on Major League Hacking News.

Free Events in December 2023 to Help You Learn About the Cloud

MLH Team — Thu, 30 Nov 2023 23:30:52 +0000

Why learn about cloud technology?

Cloud services provide access to a wide range of tools and resources that aid in faster application development, fostering an environment conducive to innovation and experimentation.

Understanding cloud technology equips new developers with essential skills in deploying, managing, and optimizing applications, enhancing their employability and adaptability in the rapidly evolving tech industry. Embracing cloud technologies early in your career will allow you to stay abreast of industry trends and prepare you to contribute effectively to projects and businesses leveraging cloud-based solutions in your first internship.

Upcoming Cloud Events

1) Global Hack Week: Cloud, Virtual, December 4th-11th

Join thousands of hackers globally for a week of 50+ free workshops designed to help you learn about cloud technology. Learn how to use popular tools like Google Cloud, Firebase, and Docker.

Below is a preview of the schedule!
⭐ Max Out Your Hack With Google Cloud
⭐ Firebase Apps 101
⭐ Intro to the Cloud
⭐ Docker Deployment 101: Deploying Applications
⭐ Building a Mobile App with Google Cloud Tools

2) AWS Dev Days, Virtual, On-Demand

AWS Dev Days are free virtual events where people of technical backgrounds can dive deep into some of the hottest and most advanced topics in cloud computing. Join them for demo-focused technical sessions with AWS technical experts!

3) Google Cloud Fundamentals: Big Data and Machine Learning, Virtual, December 8th

This 1-day course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Google offers a variety of virtual cloud courses for developers of all skill levels. They also offer courses in different languages! You can filter the list to help you find the right course for you on Google's website here.

4) Cloud Academy Exam Prep Webinars, Virtual, On-Demand

If you're a step ahead and already looking to get your cloud certifications, we found this awesome resource for you! In this virtual and on-demand series, instructors from all the big cloud companies (Google, Microsoft, AWS) share how to prepare and ace their exams.

Recap

If you want to learn about the cloud, there are a wide variety of free events and resources available to help you learn. You are not alone on your learning journey! Happy hacking!

What cloud events did we miss? Let me know, so we can build out this list.

2023 Edition: New & Upcoming Hackathons

MLH Team — Mon, 30 Oct 2023 19:05:38 +0000

Hackathons are a great opportunity for developers in all stages of their careers to build their resumes, grow their networks, and get hands-on technical experience.

Below are a few upcoming hackathons in 2023, we recommend checking out! For the most up-to-date list of upcoming hackathons, visit the MLH website.

DandyHacks – Rochester, New York – November 3rd-5th
TechTogether Online – Worldwide – November 3rd-5th
YCP Hacks – York, Pennsylvania – November 3rd-5th
hackCBS – Delhi, India – November 4th-5th
HackRPI – Troy, New York, November 4th-5th
HackUTD – Dallas, Texas – November 4th-5th
Make-a-Ton 6.0 – Kochi, Kerala – November 4th-5th
MakeUC – Cincinnati, Ohio – November 4th-5th
NewHacks – Toronto, Canada – November 4th-5th
MadHacks – Madison, Wisconsin – November 11th-12th
AI ATL – Atlanta, Georgia – November 17th-19th
BostonHacks – Boston, Massachusetts – November 18th-19th
Hack Western 10 – London, Ontario – November 24th-26th
Web3 Apps – Worldwide – November 24th-26th

2024 Upcoming Hackathons

Techacks 4.0 – Patiala, Punjab – January 19th-21st
nwHacks – Vancouver, Canada – January 20th-21st
HoyaHacks – Washington, District of Columbia – January 26th-28th
Hacklytics – Atlanta, Georgia – February 9th-11th
Hack This Fall – Gandhinagar, Gujarat – February 9th-11th
UGAHacks – Athens, Georgia – February 9th-11th
Hacklahoma – Norman, Oklahoma – February 10th-11th
cmd-f – Vancouver, Canada – March 9th-10th
WEHack – Dallas, Texas – March 16th-27th

TOP 2024 Summer Tech Internship Programs for Hackers

MLH Team — Mon, 30 Oct 2023 18:56:32 +0000

Did you know that recruiting for summer internships starts as early as September? To help you get started on your career search, we curated a list of the top internship programs in tech.

Make sure to submit your application using the referral links below, so our partners know that you're applying through MLH! Sign up for the MLH newsletter, and be the first to know about special recruiting events for hackers.

1. Fidelity LEAP Program

If you're an upcoming or recent college graduate, we recommend checking out the Fidelity LEAP program. The LEAP program provides you with 12 to 18 weeks of dedicated paid training that helps you transition to your full-time role at Fidelity.
Fidelity is currently hiring Full-Stack Engineers for their 2024 LEAP program with two available start dates: January 2024 or June 2024.

Fidelity LEAP is designed to reinforce a culture of continuous development and a growth mindset through immersive, high-impact learning experiences. You'll practice your skills on projects that tackle real-world challenges that Fidelity is facing today. Along the way you'll receive personalized coaching and mentoring, as well as the opportunity to build a meaningful, cross-company network.

2. Fidelity Intern Program

The 10-week Fidelity Intern Program is a world-class opportunity for college-level students looking to explore and learn more about Fidelity's business and culture through a cohesive and centralized program experience.

Fidelity has a wide range of internship roles every summer. They hire interns into 8 major skill areas, including a technology internship program. Fidelity offers internships in Full-Stack, Mobile, Data, or Systems engineering. The application window is currently open - apply today!

3. Google - Student Training Engineering Program (STEP)

STEP (Student Training in Engineering Program), formerly known as Engineering Practicum, is a 12-week internship for first and second-year undergraduate students with a passion for computer science.

The internship program focuses on providing development opportunities to students from historically underrepresented groups in tech, through technical training and professional development. This unique internship offers the opportunity to work on a software project alongside other STEP interns and full-time Googlers, and provides the chance to bridge the gap between academic understanding and practical professional experience.

4. Microsoft - Software Engineering Internship Program

Every year, Microsoft welcomes thousands of university students from every corner of the world to join their company. The Software Engineering Internship is just one of the many internships that Microsoft offers each year.

Originally published at https://news.mlh.io on September 18, 2023.