How to Hire PyTorch Developer

Released at the beginning of 2016 by Facebook AI PyTorch quickly made to top 3 frameworks for deep learning comprising the most efficient ML methods. Such status makes it a key requirement for a proficient machine or deep learning engineer, data scientist or other developers of Ai solutions.

6 min read, Aug 14
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PyTorch is an open-source Python-based ML library with deep integration of C++. Its agile and modular essence presented it as a suitable choice for research including experimental. It provides dynamic graph-based execution. Such concept along with more intuitive modeling gave it the advantage against widely distributed Tensorflow.

Pytorch frontend is hybrid making it appropriately flexible, scalable and stable to raise project development efficiency relinking research and release. It also uses diverse backends for CPU and GPU making it a good fit for constrained systems. Such usability allows constantly growing support community that believes and contributes to its potential.

The framework comes with high-level features including GPU-accelerated tensor computing and tape-based autograd improving the process of building deep neural networks. Various installation options including AWS and a support toolkit that includes libraries, datasets, and pre-trained models and other tools are available for a quick setup.

Focused on deep learning PyTorch application mostly matches employment of data science, computer vision, natural language and image processing in various industries like retail and eCommerce, logistics and automotive, healthcare and so on.

How to hire a PyTorch developer

If you’ve chosen PyTorch as a core framework for your project it’s important to define what kind of specialists you actually need. Although basic requirements for data scientists, ML, and CV experts, NLP or IP developers are close enough the qualification level and focus have significant differences as well as salary expectations and necessary working conditions.

Nevertheless, an appropriate candidate shouldn’t be just a PyTorch expert. One’s techniques of modeling and building neural networks should be variable and have a practical background. Self-explanatory that he or she should be one of those strong C++ and Python developers with Java, Matlab, and other specific skills.

An applicant should have a degree in Computer Science and Engineering, Statistics or other field related to the prior experience defined by the project specifics. If the planned Ai solution is an industry-oriented the background in the selected scope or leastways its comprehensive understanding should be present. It also concerns the deep learning application for computer vision and other AI scopes.

It’s desirable that algorithm implementation experience should be wider than DL and include reinforcement and un-/semi-/supervised learning that backed up by strong knowledge basis in math, machine learning, statistics, etc. whether you’re looking for deep learning research engineer or a data scientist.

What to ask PyTorch developers to hire the best ones

Considering the framework basis during a technical interview Python and C++ in addition to basic PyTorch questions should be included in a survey. It’s important that a candidate shows the understanding of the languages and library advantages for the chosen application fields and in comparison to other ones like Tensorflow or Keras.

Explore what fundamental PyTorch interview questions your dev, project manager or team lead should ask to retrieve a candidate’s potential to fulfill requirements of your project. Consider that you should add specific ones giving the concept of the developing solutions and focus of a vacant position.

  1. What is PyTorch and what comprises its ecosystem? What AI subfields is it applied in and why?
  2. What are the basics of building PyTorch Neural Networks?
  3. How and why to use C++ frontend?
  4. What are the tensor types? What is autograd?
  5. What Python compilers can be used for PyTorch projects?
  6. What are the types of deep neural networks? What are their differences and benefits?
  7. Which of them can be developed most efficiently within this Facebook AI library and why?
  8. What built-in PT DNNs are based on?
  9. What are the possibilities of Recurrent Neural Networks in the framework?
  10. What is torch.nn? What types of containers and layers are available, their purposes?
  11. What’s torch.cuda? What its semantic specifics?
  12. How to run models on GPU or CPU?
  13. Why do you think PyTorch was chosen for this project?
  14. What are PT framework pros and cons generally and in comparison to its alternatives?
  15. Why have you decided to become an expert in PyTorch and what do you do for that?

Any answer should be well-reasoned and be presented confidently. It’s needed to ensure that a candidate actually comprehends framework features, advantages, and application for software development for various platforms. Memorized theory the night before for found online for PyTorch and generally Python developers interview questions is clearly not a sufficient level of knowledge.

Where to look for PyTorch specialists

It’s important to understand that according to statistics 74 percent of such pros are involved in web development and only 6 percent of ML experts have sufficient expertise in both best deep learning frameworks - TensorFlow and PyTorch.

That’s why to have more options, it's better to consider hiring remote employees. Variants are to search freelancers on the job boards or contact PyTorch development company that specializes in outsourcing or outstaffing.

The last staffing solution usually appears more attractive considering the possibility to cut operating and management expenses and have more qualified candidates for selection initially. Considering the deep integration of the language, Python development companies should also make the list of your HR.

If talking about search regions it totally depends on the budget size and workflow scheduling. The option that will satisfy rates expectations of the SME sector and the quality level of big players is to hire PyTorch deep learning engineers in Ukraine.

The country is one of the proven leaders of the CEE IT market which representatives have vast working experience on projects worldwide in different scopes including AI. Minimum salary of Python developers in Ukraine is four times smaller than the same level US professional.

UA AI solution providers like Springs are focused on collaboration with enterprises outside the local market. That’s why they adhere to international standards and use agile methodologies to ensure development efficiency. The company follows the trends and that’s why can offer PyTorch developers to solve the real business problems.

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