AWS internships for Students in Australia

https://www.linkedin.com/pulse/how-prepare-technical-interview-google-amazon-linkedin-chris-laffra/

How to Prepare for a Technical Interview at Uber, Google, Amazon, LinkedIn, Microsoft, Facebook, etc.

The Minimal Book List
There are many good books to prepare for the technical interview itself. If you have time to only read three, I would strongly recommend the following:

The Algorithm Design Manual. A thorough, authoritative coverage of all the possible algorithms you may run into during your interview. Also discusses complexity, dynamic programming, the big-O notation, and discusses given a particular problem, what algorithm to chose. This book is considered the "Bible" by many engineers at Google.
Influence - The Psychology of Persuasion. A great set of lessons they did not teach you in Engineering School. Describes how to quickly find a common ground with a stranger, break the ice, and make people genuinely like you. During an interview you have about 3 seconds to make your first impression. This book helps to prepare in that part of the interview. Particularly useful for nerds, like me.
ACE The Programming Interview. A list of 160 questions that might be asked at technical interviews. Don't expect any of these to actually be asked at Google, as they have all been blacklisted a long time ago. However, the methodical approach explained in the book will help you practice and quickly recognise where the interviewer is leading you to, when they ask you a specific question.

Stevey's Blog - Get that job at Google

http://steve-yegge.blogspot.com/2008/03/get-that-job-at-google.html

The two best long-term warm-ups I know of are:

1) Study a data-structures and algorithms book. Why? Because it is the most likely to help you beef up on problem identification. Many interviewers are happy when you understand the broad class of question they're asking without explanation. For instance, if they ask you about coloring U.S. states in different colors, you get major bonus points if you recognize it as a graph-coloring problem, even if you don't actually remember exactly how graph-coloring works.

And if you do remember how it works, then you can probably whip through the answer pretty quickly. So your best bet, interview-prep wise, is to practice the art of recognizing that certain problem classes are best solved with certain algorithms and data structures.

My absolute favorite for this kind of interview preparation is Steven Skiena's The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace (and important) graph problems are – they should be part of every working programmer's toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. But the gold mine is the second half of the book, which is a sort of encyclopedia of 1-pagers on zillions of useful problems and various ways to solve them, without too much detail. Almost every 1-pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types.

Other interviewers I know recommend Introduction to Algorithms. It's a true classic and an invaluable resource, but it will probably take you more than 2 weeks to get through it. But if you want to come into your interviews prepped, then consider deferring your application until you've made your way through that book.

Tech Prep Tips

The best tip is: go get a computer science degree. The more computer science you have, the better. You don't have to have a CS degree, but it helps. It doesn't have to be an advanced degree, but that helps too.

However, you're probably thinking of applying to Google a little sooner than 2 to 8 years from now, so here are some shorter-term tips for you.

Algorithm Complexity: you need to know Big-O. It's a must. If you struggle with basic big-O complexity analysis, then you are almost guaranteed not to get hired. It's, like, one chapter in the beginning of one theory of computation book, so just go read it. You can do it.

Sorting: know how to sort. Don't do bubble-sort. You should know the details of at least one n*log(n) sorting algorithm, preferably two (say, quicksort and merge sort). Merge sort can be highly useful in situations where quicksort is impractical, so take a look at it.

For God's sake, don't try sorting a linked list during the interview.

Hashtables: hashtables are arguably the single most important data structure known to mankind. You absolutely have to know how they work. Again, it's like one chapter in one data structures book, so just go read about them. You should be able to implement one using only arrays in your favorite language, in about the space of one interview.

Trees: you should know about trees. I'm tellin' ya: this is basic stuff, and it's embarrassing to bring it up, but some of you out there don't know basic tree construction, traversal and manipulation algorithms. You should be familiar with binary trees, n-ary trees, and trie-trees at the very very least. Trees are probably the best source of practice problems for your long-term warmup exercises.

You should be familiar with at least one flavor of balanced binary tree, whether it's a red/black tree, a splay tree or an AVL tree. You should actually know how it's implemented.

You should know about tree traversal algorithms: BFS and DFS, and know the difference between inorder, postorder and preorder.

You might not use trees much day-to-day, but if so, it's because you're avoiding tree problems. You won't need to do that anymore once you know how they work. Study up!

Graphs

Graphs are, like, really really important. More than you think. Even if you already think they're important, it's probably more than you think.

There are three basic ways to represent a graph in memory (objects and pointers, matrix, and adjacency list), and you should familiarize yourself with each representation and its pros and cons.

You should know the basic graph traversal algorithms: breadth-first search and depth-first search. You should know their computational complexity, their tradeoffs, and how to implement them in real code.

You should try to study up on fancier algorithms, such as Dijkstra and A*, if you get a chance. They're really great for just about anything, from game programming to distributed computing to you name it. You should know them.

Whenever someone gives you a problem, think graphs. They are the most fundamental and flexible way of representing any kind of a relationship, so it's about a 50-50 shot that any interesting design problem has a graph involved in it. Make absolutely sure you can't think of a way to solve it using graphs before moving on to other solution types. This tip is important!

Other data structures

You should study up on as many other data structures and algorithms as you can fit in that big noggin of yours. You should especially know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.

You should find out what NP-complete means.

Basically, hit that data structures book hard, and try to retain as much of it as you can, and you can't go wrong.

Math

Some interviewers ask basic discrete math questions. This is more prevalent at Google than at other places I've been, and I consider it a Good Thing, even though I'm not particularly good at discrete math. We're surrounded by counting problems, probability problems, and other Discrete Math 101 situations, and those innumerate among us blithely hack around them without knowing what we're doing.

Don't get mad if the interviewer asks math questions. Do your best. Your best will be a heck of a lot better if you spend some time before the interview refreshing your memory on (or teaching yourself) the essentials of combinatorics and probability. You should be familiar with n-choose-k problems and their ilk – the more the better.

I know, I know, you're short on time. But this tip can really help make the difference between a "we're not sure" and a "let's hire her". And it's actually not all that bad – discrete math doesn't use much of the high-school math you studied and forgot. It starts back with elementary-school math and builds up from there, so you can probably pick up what you need for interviews in a couple of days of intense study.

Sadly, I don't have a good recommendation for a Discrete Math book, so if you do, please mention it in the comments. Thanks.

Operating Systems

This is just a plug, from me, for you to know about processes, threads and concurrency issues. A lot of interviewers ask about that stuff, and it's pretty fundamental, so you should know it. Know about locks and mutexes and semaphores and monitors and how they work. Know about deadlock and livelock and how to avoid them. Know what resources a processes needs, and a thread needs, and how context switching works, and how it's initiated by the operating system and underlying hardware. Know a little about scheduling. The world is rapidly moving towards multi-core, and you'll be a dinosaur in a real hurry if you don't understand the fundamentals of "modern" (which is to say, "kinda broken") concurrency constructs.

The best, most practical book I've ever personally read on the subject is Doug Lea's Concurrent Programming in Java. It got me the most bang per page. There are obviously lots of other books on concurrency. I'd avoid the academic ones and focus on the practical stuff, since it's most likely to get asked in interviews.

Coding

You should know at least one programming language really well, and it should preferably be C++ or Java. C# is OK too, since it's pretty similar to Java. You will be expected to write some code in at least some of your interviews. You will be expected to know a fair amount of detail about your favorite programming language.

Other Stuff

Because of the rules I outlined above, it's still possible that you'll get Interviewer A, and none of the stuff you've studied from these tips will be directly useful (except being warmed up.) If so, just do your best. Worst case, you can always come back in 6-12 months, right? Might seem like a long time, but I assure you it will go by in a flash.

The stuff I've covered is actually mostly red-flags: stuff that really worries people if you don't know it. The discrete math is potentially optional, but somewhat risky if you don't know the first thing about it. Everything else I've mentioned you should know cold, and then you'll at least be prepped for the baseline interview level. It could be a lot harder than that, depending on the interviewer, or it could be easy.

It just depends on how lucky you are. Are you feeling lucky? Then give it a try!

 

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AWS

Network Development Engineer Intern

Applied Scientist - Intern

Software Development Engineer Intern

BASIC QUALIFICATIONS

· In your penultimate year studying a bachelor's degree in Computer Science or equivalent. ( finishing in 2021)
· Programming experience with one or more of the following languages - Java, Python, Go, C++, or C#.
· Computer Science fundamentals in object-oriented design, data structures, algorithm design and complexity analysis.
· Excellent written and verbal communication skills
· Strong willingness to make a difference.
· Australian citizenship or PR as we do not sponsor visas for internships

PREFERRED QUALIFICATIONS

· Exposure to large-scale distributed storage and database systems (e.g. SQL, NoSQL, Graph Databases)
· Knowledge of professional software engineering best practices for the full software development life cycle; including coding standards, code reviews, source control management, build processes, testing, and operations.
· Exposure to AWS services such as RDS, EC2, Dynamo DB, CloudWatch.