Machine Learning Engineer

A machine learning engineer is typically a member of a data science team. Working with data scientists and software engineers, a machine learning engineer uses expertise in programming, mathematics, and statistics to help build intelligent systems capable of learning from and making predictions from data. Machine learning engineers play a crucial role in bridging the gap between research and practical applications.

What Does a Machine Learning Engineer Do?

One of the primary tasks of a machine learning engineer is data preprocessing and feature engineering. A machine learning engineer works with a large dataset, cleans and preprocesses the data, and extracts important features from that dataset that can then be used to train a machine learning model. Depending on the problem at hand, the machine learning engineer needs some expertise in selecting the appropriate algorithm. Then, they’ll fine-tune, repair, and optimize the data in the algorithm to help a computer learn a specific task, then repeat that task over and over with improved speed, efficiency, and accuracy.

Examples of Machine Learning Engineer Responsibilities:

Let’s take a look at a potential machine learning model and consider what the machine learning engineer has to do in order to make this model helpful:

For example, imagine we are doing a research project and we need to classify hundreds of thousands of images of birds. It's a boring process if you are a human. Penguin photos go in this pile. Ostrich photos go in that one. The ideal solution would be to train a computer to do the sorting for us. But first, we need the computer to be able to accurately distinguish between penguins and ostriches.

Training a Model:

1. To train the model, a machine learning engineer would typically start by gathering a large dataset of labeled images. This dataset would include thousands of images of both penguins and ostriches, with each image correctly labeled as either "penguin" or "ostrich." The dataset needs to be diverse and representative of the variations and characteristics of both penguins and ostriches.

2. The next step involves preprocessing the data. The machine learning engineer would resize and normalize the images to ensure consistent dimensions and pixel values. This preprocessing step helps in reducing noise and making the images suitable for training.

3. Once the data is preprocessed, the machine learning engineer would split the dataset into two parts: a training set and a validation set. The training set, typically comprising around 70-80% of the data, is used to train the model, while the validation set, containing the remaining 20-30%, is used to evaluate the model's performance during training and tune hyperparameters.

4. Next, the machine learning engineer selects an appropriate algorithm, such as a convolutional neural network (CNN), which is widely used for image classification tasks. They design the architecture of the CNN, specifying the number and type of layers, activation functions, and optimization algorithms.

Training a Computer:

The training process begins by feeding the training set of labeled images into the CNN. The model adjusts its internal parameters (weights and biases) based on the training data and the labels already given to the photos. This adjustment is done through a process called backpropagation, where the errors between predicted and true labels are used to update the model's parameters. The training continues for multiple epochs, with each epoch involving the entire training dataset. After each epoch, the machine learning engineer evaluates the model's performance on the validation set. They monitor metrics such as accuracy, precision, and recall to assess how well the model is learning to differentiate between penguins and ostriches.

The training process continues until the model achieves satisfactory performance. Once the training is complete, the machine learning engineer can test the trained model on unseen images, allowing it to make predictions on new penguin and ostrich images with a certain level of accuracy. If successful, the computer can do the work of sorting through the hundreds of thousands of images so a human doesn't have to. If we can trust the computer to do that job well, the human can put his or her time and effort into a bigger, more complex problem. The machine learning engineer has helped a machine "learn" to do something really bored graduate students were doing before.

More Examples of Machine Learning:

If that example seems too tedious and useless, let’s consider another:

Angelique works for a healthcare technology company. Her primary focus is on developing machine learning models that can predict disease outcomes based on patient data. Angelique spends a significant amount of time analyzing and preprocessing medical data, ensuring that it is accurate and adheres to privacy regulations. She then designs and trains deep learning models to classify patient data, helping doctors make informed decisions about treatments and interventions.

And here’s one more example: Alejandro is employed by an e-commerce company. His main responsibility is to enhance the recommendation system to personalize product suggestions for customers. Will 20-year-old women prefer this mascara or that one? Does the color of the packaging influence their purchases? How many of them can be persuaded to purchase eyeliner in addition to mascara if the algorithm adds a coupon at the right moment?

Alejandro collaborates closely with data scientists and software engineers to extract and analyze user behavior data, such as browsing history and purchase patterns. Using this data, Alex builds machine learning models that can predict user preferences and provide personalized product recommendations in real-time.

Whether it's healthcare, e-commerce, finance, or any other field, machine learning engineers apply their expertise to build intelligent systems that can improve decision-making and automate complex tasks. As of this writing (2023), the median annual salary for a machine learning engineer working in the United States is between $125,000 and $130,000.