Remote back-end ML engineer jobs

The back-end Machine Learning Engineer is a research programmer who controls software to carry out predictive models. An Engineer of Machine Learning creates AI systems that use major data sets to produce and build algorithms capable of learning and predicting things. To help make high-performance machine learning models, the Back-end Machine Learning Engineer must look at, analyze and organize data, run tests, and optimize the learning process.

If you're interested in data, automation, and algorithms, machine learning is the appropriate career choice for you. Every day, you will move vast volumes of raw data, build algorithms to process it and automate the system for optimization.

Here is how you can become a professional back-end ML engineer.

Machine learning is a critical element of AI; it's the study of computer algorithms and statistical models that systems use to effectively perform a specific task without explicit instructions. Machine learning is one of the most exciting and in-demand areas of Data Science, but not the only one.

There are many applications for machine learning, including robotics, natural language processing, image recognition, and more. Back-end Machine Learning Engineers are in high demand across industries around the world, making this career path a solid option for those interested in getting into AI. As companies find new uses for machine learning technology in everything from health care to entertainment, they'll need workers who can help improve their ML systems.

The roles and responsibilities of a Back-end ML engineer include:

• Developing back-end infrastructure, data pipelines, and machine learning models for our AI-based products
• Automate modeling pipelines and build working ranking models
• Cooperate with product teams and engineers (especially Front-end engineers)
• The development, testing, deployment, maintenance, and improvement of machine learning software
• Assess, define and apply advanced machine learning algorithms to text and unstructured data
• Research on new advances in natural language processing
• Develop and maintain the ML and backend codebases
• Ensure data security and protection
• Building and experimenting with APIs, storage solutions, and other engineering projects

A Back-end Machine Learning Engineer is a position where you’ll be in charge of designing machine learning applications and systems. This includes analyzing and organizing data, running tests and experiments, and generally monitoring and optimizing the learning process to develop high-performing ML systems. A few key prerequisites are being proficient at coding in Python, being able to keep track of several moving parts at once, and having the ability to build predictive models.

In this role, you'll be responsible for building machine learning models using data emerging from web applications and other sources. Prior expertise in programming will be useful, as you'll need to apply algorithms to the data your models gather. Applicants with the requisite combination of mathematical background, statistical analysis abilities, and web development experience are encouraged to apply.

Now, let's look at the skills and methods you'll need to master in order to become a successful Back-end ML engineer:

The first step is to learn the fundamental skills you need to land a high-paying Back-end ML engineer job. Here's what you need to know!

1. Machine Learning algorithms

A Machine Learning Engineer should be comfortable with all the common machine learning facilities. It is essential for an ML engineer to know how and where the algorithms are used. The three most common types of ML algorithms are supervised, unsupervised, and reinforcement machine learning algorithms. Some of the more common ones are Naive Bayes Classifier, K Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, and others. So it's good if they have a sound knowledge of all these algorithms before starting their ML engineering project.

2. Data modeling and evaluation:

Data modeling and evaluation are crucial concepts in machine learning. It is one of the first steps taken by an ML engineer because data needs to be transformed and shaped before it can be used to train the system. You must be able to understand the data's fundamental structure, then look for patterns that aren't visible to the naked eye. For example, regression, classification, clustering, dimension reduction, and other machine learning methods require accurate and varied data sets. A professional ML engineer must be able to identify patterns in data as well as apply various techniques for model building.

3. Neural Networks

In the current era where machine learning is ruling, it’s crucial for every machine learning engineer to understand the basics of neural networks by heart. Neural networks are nothing but collections of artificial neurons which are interconnected and generate outputs based on inputs received with an activation function.

4. Natural Language Processing (NLP)

Natural Language Processing (NLP) is an integral part of the Artificial Intelligence revolution. It enables machines to process human communication, allowing them to hear and understand the context of language. In essence, it teaches computers human language by breaking down texts into its grammar to extract phrases, extract keywords and delete superfluous words. The most popular NLP platform is called the Natural Language Toolkit (NLTK). This library contains a number of functions that help computers process natural language.

5. Applied mathematics

Math is one of the fundamental components of a Machine Learning engineer. It gives them the skills to define parameters and predict confidence levels. As a matter of fact, the application of various mathematical formulas helps in choosing the best machine learning method for a given set of data. In addition to this, there are extremely well-developed statistical modeling processes in machine learning algorithms. Mathematical concepts such as linear algebra, probability, statistical inference, etc., give an ML engineer more control over datasets and tools.

Practicing is a crucial step to becoming a better developer. The more you practice, the more skills will grow over time. Make sure that you have someone who can help you out when you need it and keep an eye on what kinds of problems are coming up for them so they can give advice about how to work through them! In addition to this, there needs to be sufficient time allocated toward work-life balance so that developers don't burn out.

Turing has the best remote Back-end ML engineer jobs that will fit your career goals as a Back-end ML engineer. Grow quickly by working on difficult technical and business problems using cutting-edge technology. Join a network of the world's best developers to find full-time, long-term remote Back-end ML engineer jobs with better pay and opportunities for advancement.

Every Back-end ML engineer at Turing has the ability to set their own rate. However, Turing will recommend a salary at which we are confident we can find you a fruitful and long-term opportunity. Our recommendations are based on our assessment of market conditions as well as customer demand.