How can a student without any experience outside the classroom pique the interest of a hiring manager?
Have a project that:
1. Is eye catching when boiled down to a few bullets points on a resume
2. Leads to an engaging 20 minute conversation
As a hiring manager looking for machine learning researchers, I’ve reviewed 1000’s of resumes and conducted 100’s of interviews, and the toughest resumes for me to evaluate remain new grads without internships or publications.
Why? Let me compare how my initial conversations go with different types of candidates.
- Experienced candidate with a prior job: “Let’s chat about when you deployed X network in production to achieve result Y”.
- New grad with an internship: “Give me more details about what you did for company C during the summer.”
- New grad with a paper: “I was reading your paper, and I had a question about ablation study A”.
- Other new grads: “Let me look over your resume…, um yeah, I guess I’ll try and pay attention as you tell me about yet another class project that involved applying a pretrained ResNet model to MNIST.”
At this point, there are enough students doing ML that it is no longer sufficient to stand out by just having ML classes on your resume. But if you can get an internship or publication, that continues to stand out. So are you screwed without an internship or pub? Not at all! But you do need to do some work to spice up your class or capstone projects.
What can you do to make a project that stands out? Below are a few ideas biased towards my work on neural networks for real time embedded systems.
- Open source code. An estimated 10% of published papers have open sourced code. So take a cool new paper and code it up! Here is a very impressive repo that is well beyond a class project, but for a simpler project code up one single paper.
- Faster. Most academic papers and leader boards focus on performance, often to the detriment of runtime. I’m always impressed when someone can take a paper and speed it up with minimal drop in performance. Some ways to speed up networks include changing the architecture, pruning, and quantization.
- Smaller. Networks are massively over parametrized and much larger than they need to be. Grab a network, squeeze it down, and show you can fit it into an edge device. Check out SqueezeNet and the Lottery Ticket Hypothesis for interesting papers in this area.
- Cheaper. Training state of the art neural networks is extremely time consuming and costly. Demonstrate how to train a network with a limited GPU hour budget and still get reasonable performance. Check out some ideas from Fast.ai for fast training and this article for training on a single GPU.
- Multitask. Academic networks are usually single task, but real time networks are usually Frankensteins with a shared feature map supporting multiple tasks. I recommend this review paper as well as this more recent paper to get started.
Hope that helps! I look forward to chatting with you about these cool projects!