Photo by Mikhail Nilov
As an experienced ML developer who has hired numerous junior engineers for different projects, I’ve learned that there are specific skills that are essential for a junior developer to be considered for a job in the field. While these skills vary depending on the project and the company, there are universal skills that are necessary. In this article, we will discuss the key skills that junior ML developers should have to be successful in their job search. By the end of this article, you will have a better understanding of the necessary skills for junior ML developers to land their first job.
Most junior developers looking to land their first job often come from other fields and have completed some ML courses. They have a basic understanding of ML but lack a deep background in engineering, computer science, or mathematics. However, a solid background in math is highly recommended for success in ML, as it is essential for experimentation and fine-tuning algorithms. College students with a math degree are at an advantage here, but they often lack practical skills and experience vital for a job. Most applicants for junior ML engineer positions are proficient in SQL, vector embeddings, and basic time series analysis algorithms. They also have basic Python library knowledge, but it’s generally not enough for success in the field.
Popular courses often fail to provide hands-on experience and a deeper understanding of key subjects in ML. Therefore, junior developers need to learn valuable skills on their own to make themselves more marketable. Skills such as random forests, web scraping, creating user-friendly interfaces, deploying models to production environments, using Docker for containerization, managing dependencies with virtual environments, effectively using GitHub for version control, and ML tracking systems like Weights and Biases or MLFlow are all vital for success in the field. By mastering these skills, junior developers can set themselves apart from the competition and become valuable assets to any ML team.
To gain experience in ML and programming, creating pet projects can be immensely helpful. It demonstrates programming skills, ML knowledge, and motivation to potential employers. Pet projects could include web scraping, NLP, computer vision, sound, and time series prediction projects. Additionally, developing soft skills such as effective communication, public speaking, and explaining technical concepts to varying audiences is also important for success in the ML field. Ultimately, a strong portfolio showcasing these skills is as valuable, if not more valuable, than a degree from a prestigious university.