As AI swoops into our lives, more and more sourcers are challenged with various AI positions.
After successfully finishing a challenging Deep Learning sourcing project, I’m glad to share how I dove Deep into Deep Learning Engineer and Researcher positions and leave you with a short and sweet tutorial that you can easily use.
We all encounter AI (Artificial Intelligence), in its different forms, almost on a daily basis. It might possibly be the most common buzzword of this age. As technical talent sourcers, we should have a good understanding of what it means so we can be a truly effective Growth Partner to our clients and design the best and most precise sourcing process. Furthermore, we should learn and understand it in order to use it as a sourcing tool.
Once you’ve fully understood your client’s products, needs, and atmosphere (stay tuned for another article dedicated to this matter) it’s time to execute your sourcing strategy. So, come on, get your scuba gear, and let’s dive deep into Deep Learning sourcing.
#1 — Understanding the Basics — What is Deep Learning?
Deep Learning (DL) is a sub-subset of AI –
To make it more tangible — In image processing, the lower layers locate edges, while the higher layers recognize human-relevant concepts, like digits, letters, or faces.
Driverless cars, for example, rely on deep learning technology to identify and differentiate between objects such as traffic signs and pedestrians.
#2 — Knowing your titles
Deep Learning Researcher — The DL researcher works on how to build the network (as a neural network) and train the data on it. The demand is usually experience in significant DL research, which can also be within the framework of a Master’s or PhD with a leading researcher.
Deep Learning Engineers — These are mostly engineers who started their way with software development and then shifted towards Machine Learning (ML) and eventually Deep Learning, developing algorithms that work with the data received from the brain. It’s a relatively new position, there aren’t a lot of SW engineers who’ve made this shift. Hence, the title might be confusing and not accurate.
- You would want to look out for some of these titles: Deep Learning Researcher / Machine Learning Researcher / Computer Vision Researcher / Algorithm Developer / AI Developer / Data Scientist / Applied Researcher / Applied Scientists / Research Scientist.
- Be sure to keep an open mind, you might find your star candidate under the title you’d least expect to.
#3 — Target the right companies — Industry Experience
Deep Learning researchers and engineers are much less “out there” than other technologists, so my suggestion is to start with a wide “Deep Learning” search on LinkedIn, but bound to specific companies known for their DL products or departments.
- The most known and with the most candidates to target are probably these — Google Research / Amazon Labs / Facebook Reality Labs / Alibaba / Microsoft (Hololense) / Apple / Snap.
- You certainly wouldn’t want to stop there. When I first made the DL sourcing tutorial a while ago, I naturally used and referred to Google search for AI-oriented companies. But now, that ChatGPT and other AI tools have stormed into our lives, you should definitely use them in order to prepare a great sourcing infrastructure. You can ask ChatGPT to generate a list of AI companies in the geographic area that is relevant for you, you can ask it to generate a list of companies that use Deep Learning technologies, or that hire Deep Learning researchers and engineers, etc. Keep in mind that these AI tools are not foolproof, you should treat the answers you get with caution and verify and investigate them further.
- One of the websites I found helpful is AI Startups, and what I like best about it is that it’s categorized by country, technology, and applications. Bear in mind that not every ML product, department, engineer, or researcher uses DL, but you’d definitely want to broaden your search there.
- Look at every profile as a gate to finding more companies and profiles. Take notice of past experience and connections. Examining these profiles will potentially lead you to less-known under-the-radar AI Startups that you can add to your target list.
- Run a search based on past companies, especially if you have critical relevant AI companies on your client’s blacklist.
- I recommend combing your search with a sales navigator or recruiter search.
#4 — Target the Right Technologies
While sourcing Deep Learning engineers you want to know your technologies, so those “under the radar” profiles won’t dodge yours. Be on the lookout for the right skills…
- PYTHON is the most demanded programming language in ML and DL. It uses one of the most natural languages and less complicated syntax and has versatile libraries and frameworks that make coding easy.
- PyTorch is one of the most common frameworks for creating deep neural networks. It’s open-source, based on Python, and built to be integrated into it.
- TensorFlow is an open-sourced end-to-end platform, that’s also commonly used for multiple ML tasks, as well as Keras, which is a high-level neural network library that runs on top of TensorFlow and is built-in Python.
- Other technologies that can be found are NumPy and SciPy (important libraries in Python that have a wide range of functions and are used to perform various operations with the data | R (open-source programming language used for statistics and helps visualize statistical data by use of graphics) | C++ (more complicated, less user friendly and lacks a framework support system).
#5 — Academia
You would usually need your DL candidates to be with an academic degree. But as for the researcher position, it is a MUST. You should look out for –
- MSc and Ph.D., usually in Computer Science, Physics, Math, or Electric Engineering.
- Indications of academic excellence.
- Published articles and patents.
- In Israel, research degrees studied from 2015 onwards are highly likely to include Deep Learning studies and research.
- Acclaimed mentors for MScs and PhDs also indicate certain expertise.
I highly suggest using an Xray search in order to find relevant profiles that mention these mentors.
For example see this Xray for candidates mentored by Prof. Lior Wolf, an award-winning research scientist whose name precedes him in the field of Deep Learning, Computer Vision, etc.
site:il.linkedin.com/in (“Deep Learning” OR DL) “Prof. Lior Wolf”
site:il.linkedin.com/in “Prof. Lior Wolf”
#6 — Connections
I encourage you to connect to as many CTOs and Founders of startups using AI, as well as “Heads of” and Directors of AI as possible, and use those connections in order to investigate and source candidates from their connections. More often than not, it will probably prove itself to be fruitful.
#7 — Outside of LinkedIn
Diversify and refresh your search and use various platforms, such as:
- Facebook groups (E.g. Artificial Intelligence & Deep Learning / Artificial intelligence, Machine learning, Deep learning / Machine & Deep learning Israel / Deep Learning / AI / Artificial Intelligence | Machine Learning | Data Science | Deep Learning)
- Researchgate ( a European commercial social networking site for scientists and researchers to share papers, ask and answer questions, and find collaborators)
- Kaggel (a platform to learn Data Science and build a portfolio)
- Github
- Meetups
- Online Forums
You’re more than welcome to share your “Outside of LinkedIn” sources in the comments section!
Don’t forget, AI is everywhere! As I mentioned at the beginning of this article, reality obliges us to learn, understand and use AI, so use the tools it gives you for your AI positions as well!
