{ "source_file": "0930", "source_platform": "Medium", "author": "Kurtis Pykes", "title_listed": "The Traits That Will Make You Stand Out As A Machine Learning Engineer", "keywords": "MLE,职业发展", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/the-traits-that-will-make-you-stand-out-as-a-machine-learning-engineer-f0e142c6908a/", "publish_date": "2021-08", "corpus_stage": "预训练/SFT", "notes": "", "id": "95c0e11bb7f8", "collected_at": "2026-10-02T00:19:38", "title": "The Traits That Will Make You Stand Out As A Machine Learning Engineer | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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For instance, when I wanted to improve my dribbling skills in Football, I went to the GOAT (Greatest of all Time), Lionel Messi. My study didn't only consist of drooling over his God-like abilities with a Football, but also a deep dive into understanding: 1) his thought process so I understand when he chooses his moments to dribble or pass and 2) how he got that good at dribbling the first place. This skill has stuck with me and has made it easier for me to clone people when I want to learn various skills.\n> \"If I have seen further it is by standing on the shoulders of Giants.\" - Isaac Newton\nMachine Learning is very different from Football. First of all, machine learning is not a sport. It's not as easy to watch how the greatest machine learning engineers craft solutions unless you team up with them in a competition like Kaggle or you work in the same team as them. Both, though not impossible, are quite far-fetched. However, it is more feasible to enter into their thought process because of the first trait I want to speak about.\nHere are 3 standout traits I've seen in the most successful Software Engineers:\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) and [Computer Science](https://roadmap.sh/computer-science) roadmaps.\n### Communication\nThe ability to communicate effectively is powerful; Communication is what brings people together because others can gain insight into one's feelings, opinions, and ideas.\nThink of some of the great Machine Learning Engineers/Data Scientists (I know there's a difference);\n_Have you heard [Andrew Ng explain backpropagation](https://www.youtube.com/watch?v=x_Eamf8MHwU)?_\n_Have you heard_[ _Cassie Kozyrkov teach P-values_](https://www.youtube.com/watch?v=9jW9G8MO4PQ) _?_\n_Have you read how_[ _Aurelien Geron explained a machine learning pipeline_](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) _?_\n_Have you seen how_[ _Andriy Burkov breaks down the Machine Learning workflow_](http://mlebook.com/) _?_\nOnce you've delved into these people's work you almost feel silly for ever being confused - even if the topic is difficult.\nIt's no doubt that these people know their stuff, but the ability to articulate their thoughts in an accessible way is a skill that can be learned. In fact, it's a skill that must be learned for the simple fact that data teams are not made up of only MLEs and Data scientists, but all parties must be on the same page. The better you are at communicating, the more your light shines.\n### Skepticism\nMachine learning algorithms are trained to uncover the underlying structure of data so that it may be used to generalize to new instances. Assuming that a machine learning algorithm will make all the right decisions because they are developed by highly intellectual people is extremely naive, especially when the decisions being made affect the wellbeing of others (i.e. predicting whether someone has cancer or not).\n> \"I do not want to believe, I want to know\" - Carl Sagan\nBeing skeptical helps MLEs and Data Scientists to remain objective when they are performing machine learning. This inherent trait nudges practitioners to extensively examine the claims being made by 1) themselves (based on feature engineering or lack thereof) and 2) the model. If things go right or wrong, it's good to have evidence detailing why it happens so the process can be discarded or replicated.\nUltimately, skepticism keeps us objective which is a necessary trait to have if we wish to accurately model how things work in the real world.\n### Perseverance\nRecently, I've been reading a lot of stories by people who have tried (some failed and some succeeded) to become a Machine Learning engineer at one of the FAANG companies. It's no doubt that some of the brightest minds in machine learning work there. Although I have no interest in working at any of them, I was keen to understand their hiring process based on a quote I read by Mark Zuckerburg.\n> \"I will only hire someone to work directly for me if I would work for that person\" - Mark Zuckerberg\nAccording to [Rahul Agarwal](https://towardsdatascience.com/the-traits-that-will-make-you-stand-out-as-a-machine-learning-engineer-f0e142c6908a/None), the Facebook hiring process looks something like this:\n * Phone Screening\n * Onsite Coding Round\n * Onsite Coding Round 2\n * System Design (inc. ML system Design)\n * Behavioral\n\n\nFAANG companies seek to weed out candidates **they believe** will not fit the bill at their company, and this makes sense. The interview process is meant to be hard because the cost of hiring the wrong engineer is significantly higher than the cost of rejecting an excellent one. In my opinion, the vigorous interview process is a deeper look at one's character, specifically perseverance and leadership.\nThat is just an example of being hired at FAANG.\nThroughout your machine learning career, you'll need perseverance. Your daily activities will need you to preserve. Keeping up to date with the latest technology will require you to persevere.\nAlso, You will fail. A LOT! For example, since we can't know the best model before we start modeling data, we would have to conduct many sub-par experiments to enhance our understanding of the data and build a sufficient model. Let's face it, failing isn't fun - nobody goes home and yells \"_Hunny, I'm so happy. I failed so much today_ \", but in our field failure is almost necessary in order to succeed.\n### Final Thoughts\nTechnical ability is expected of a Machine Learning Engineer - At the end of the day, it's your job. Taking time to craft extra abilities that enhance your character is what I believe makes MLEs stand out. A simple solution to all MLEs that want to distinguish themselves from the crowd would be to learn leadership traits as they instantly jump out of the crowd.\nThanks for Reading!\nIf you enjoyed this article, connect with me by subscribing **** to my** FRE**E [weekly newsletter](https://mailchi.mp/ef1f7700a873/sign-up). Never miss a post I make about Artificial Intelligence, Data Science, and Freelancing.\n### Related Articles\n> [**The Day To Day Of A Machine Learning Engineer**](https://towardsdatascience.com/the-day-to-day-of-a-machine-learning-engineer-378b80bf1f6f)\n> [**The Difference Between Data Scientists and ML Engineers**](https://towardsdatascience.com/the-difference-between-data-scientists-and-ml-engineers-f6e797948003)\n> [**How To Become A Machine Learning Engineer**](https://towardsdatascience.com/how-to-become-a-machine-learning-engineer-e420e134c0a3)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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