Hire ML/NLP Engineers
With the development of machine learning and natural language processing, a career in ML/NLP is becoming more and more feasible. A study conducted by Indeed found that the most sought after, rapidly growing, and highly paid profession is that of a Machine Learning Engineer. The reason for the lucrative nature of these positions is due to the high demand for Machine Learning professionals and the limited supply.
Due to the need for a thorough understanding of computer programming, statistics, and data analysis, Machine Learning/Natural Language Processing Engineering positions offer a promising career path. Such positions may also include roles of leadership in automation or analytics settings that make use of data science, big data analysis, Artificial Intelligence integration, and other related techniques.
Natural Language Processing (NLP) is an increasingly popular field of study that is being used by engineers to solve a wide range of tasks, such as voice recognition, sentiment analysis, translation, grammar auto-correction while typing, and automated response generation. Although this field of study is challenging to grasp due to the complexity and variability of human language, there is a great demand for those who possess knowledge in machine learning and natural language processing. With a strong understanding of the fundamentals and principles of NLP, one will be able to interact and collaborate with highly respected companies in this field.
As businesses become more reliant on the internet for their operations, the demand for qualified remote Machine Learning and Natural Language Processing (ML/NLP) engineers is increasing. Those who understand how machine learning can help organisations achieve their objectives and goals may be able to develop the necessary skills to become successful ML/NLP engineers. With the right training and expertise, individuals can become proficient engineers, allowing them to take advantage of the growing number of opportunities available in this exciting and innovative field.
What exactly is the scope of ML/NLP engineering?
In comparison to other professional fields, machine learning has a capacity to provide opportunities on a global scale. According to Gartner, by 2022, an estimated 2.3 million jobs are expected to be created due to the advances of artificial intelligence and machine learning. This provides a clear indication of the wide range of jobs available in the area of machine learning. Additionally, the development of natural language processing (NLP) has opened up promising job prospects in the field of language technology.
The sustained advancements in the computing power have driven the growth of Natural Language Processing (NLP) even further. Over the years, NLP has evolved considerably and industry experts foresee that its utilisation will remain a significant aspect of big data technology even in the year 2022. These reports conclusively illustrate the potential of ML/NLP engineering professions in the years to come.
Do you want to work as a remote ML/NLP engineer? Let us now go through the specifics to understand more about the different parts.
What are an ML/NLP engineer’s tasks and responsibilities?
As an experienced Machine Learning/Natural Language Processing (ML/NLP) developer, you will be responsible for leveraging data to create and train models. These models will subsequently be used to automate a variety of tasks, such as image classification, voice recognition, and market forecasting. Additionally, you may be tasked with other related duties.
As an ML/NLP engineer, you will be responsible for developing gadgets and systems that are capable of understanding and processing human speech. This will involve breaking down language into its component parts, exploring the relationships between the structures, and investigating how the individual components come together to create meaning.
Let’s go through what you’ll be doing after you’ve secured remote ML/NLP engineer gigs.
- Define the datasets that will be used to train and assess the model.
- Define validation methods and put data models to use.
- Train and fine-tune data models’ hyperparameters.
- Conduct statistical analysis and model optimisation.
- Extend and maintain machine learning libraries and frameworks
How does one go about becoming an ML/NLP engineer?
Gaining proficiency in Python and R coding is the most essential step in learning machine learning. Once you have a solid foundation, you can explore various online learning opportunities such as Coursera and Udemy to further expand your knowledge. Moreover, you should start a personal machine learning project to gain hands-on experience. Although online courses can provide theoretical understanding, there is no substitute for actually working on a project. Simultaneously, you should also focus on gathering the data that is required for your project.
Once you have completed your degree in Machine Learning, it is advisable to take advantage of the opportunity to join online machine learning communities or to participate in a contest. This will not only give you the chance to test your skills, but also to meet other professionals in the field and build connections that could be beneficial for your career. Additionally, it may be beneficial to research and familiarise yourself with NLP essential techniques and topics in math, statistics, and probability prior to applying for internships or jobs. Doing so will ensure that you are equipped with an informed Machine Learning/Natural Language Processing developer CV that will make you more competitive in the selection process.
If you put in the necessary effort to hone your skills and gain relevant experience, you will be in an excellent position to pursue and secure a remote Machine Learning/Natural Language Processing engineering job. With the right preparation and dedication, there is no limit to what you can achieve.
Let’s take a look at the abilities and techniques that organisations look for when recruiting ML/NLP developers.
Qualifications for becoming an ML/NLP engineer
The first step in obtaining remote ML/NLP engineer employment is to learn the essential abilities. Let’s have a look at them now.
Machine learning algorithmsHaving a thorough understanding of the common machine learning algorithms is an essential skill for any ML/NLP engineer. There are three main types of algorithms: supervised, unsupervised, and reinforcement learning. Examples of such algorithms include Naive Bayes Classifier, K-Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, Random Forests, and more. As such, it is highly recommended that aspiring ML/NLP developers familiarise themselves with these algorithms prior to seeking employment opportunities. This will enable them to make a positive impression during remote ML/NLP developer job interviews and increase their chances of success.
Data modelling and analysisAs an ML/NLP engineer, it is essential that you are able to effectively model and assess data. As you are aware, data is a crucial component of the job. To properly model data, you must first understand its fundamental structure and then search for patterns that may not be immediately evident. Additionally, you must employ a data-appropriate technique; regression, classification, clustering, dimension reduction and other machine learning approaches are all examples of data-dependent techniques. K-mode is one example of a categorical variable clustering strategy, where k denotes a probability clustering approach. In order to successfully contribute to data modelling and evaluation, it is imperative that you possess knowledge of the various methodologies. Companies seeking remote ML/NLP developers are always on the lookout for those with expertise in such approaches, which is why they are heavily emphasised in the hiring process.
Artificial neural networksIt is undeniable that neural networks are a fundamental component in the work of a machine learning/natural language processing engineer. Neural networks are composed of several layers, with the first layer taking in data from the environment and further layers transforming this input into useful information for output. Neural networks come in a variety of forms, such as feedforward, recurrent, convolutional, modular, and radial basis function neural networks. It is not required to have an in-depth knowledge of all of these networks as a precondition for remote ML/NLP developer positions, however having some understanding of the basics is essential. As you progress, you will gain more knowledge on the subject.
Natural language processing (NLP)Having proficiency in Natural Language Processing (NLP) is essential if you aspire to work as a remote Machine Learning/NLP Developer. NLP is an area of Artificial Intelligence that focuses on teaching computers to understand human language and interpret it accurately. This is done by utilising libraries that provide different functions that help computers to break down the text into grammar, extract key phrases, and remove unnecessary words. The most popular platform used for constructing NLP applications is the Natural Language Toolkit, which may already be familiar to you.
Statistics and probabilitySome models rely on making educated guesses about certain conditions, such as n-gramme language modelling. It is important to have a strong understanding of probability and statistics in order to effectively work with and analyse corpora. Acquiring these skills is essential to accurately predicting and understanding the data in a corpus.
Linguistic expertiseBy leveraging the unique properties of nouns and verbs, as well as understanding the rules of composition for sentences and articles, you can maximise your potential as a Machine Learning/Natural Language Processing (ML/NLP) developer in the workplace. By doing so, you can ensure that you are making the most of your professional opportunities.
Programming abilitiesIt is not possible to physically hold words, therefore it is essential to be proficient in at least one programming language. It is also important to ensure that programs are capable of completing tasks quickly. Recurrent Neural Networking (RNN) is a widely researched topic in numerous disciplines, and is often employed to generate models via the use of Natural Language Processing (NLP) and Train models. To be able to use RNN effectively, it is recommended to become familiar with a few of the most popular programming languages.
How can I find remote ML/NLP engineer jobs?
The use of Machine Learning (ML) and Natural Language Processing (NLP) is expanding rapidly and is now being adopted in numerous industries, including medicine, cybersecurity, and the automotive industry. Pursuing a career as an ML/NLP Engineer is an excellent idea and a viable option for professional growth. However, it is important to keep in mind that even if you have the proper qualifications, working for a sub-par company can have a negative effect on your career.
At Works, we offer the most rewarding and stimulating ML/NLP engineer jobs that will help you reach your career objectives. Our opportunities will help you develop and expand your ML/NLP engineering skills by working on complex technical and business projects and using the latest technologies. Plus, you will benefit from joining a community of the world’s most talented developers and gain access to high-paying, long-term remote ML/NLP engineering roles with great potential for professional growth.
Responsibilities at work
- Define suitable datasets for training the model and analysing test outcomes.
- Define validation methodologies and put data models into action.
- Train and fine-tune data models’ hyperparameters
- Conduct statistical analysis and model refinement
- Extend and maintain machine learning libraries and frameworks
- Computer science bachelor’s/degree master’s (or equivalent experience)
- 3+ years of ML/NLP engineering experience (rare exceptions for skilled devs)
- Knowledge of text representation methods, statistics, and classification algorithms
- Programming languages such as Python, Java, and others.
- Knowledge of ML frameworks (Keras or PyTorch) and libraries (Scikit-learn, NLTK)
- English fluency is required for collaboration with engineering management.
- Work full-time (40 hours a week) with a 4-hour time difference with US time zones.
- Understanding of source control systems (Git, merging, branching)
- Capability to manage and analyse huge unstructured data collections
- Unix/Linux experience, including basic commands and scripting
- Working knowledge of machine learning frameworks and libraries
- Knowledge of Big Data frameworks such as Spark, Hadoop, and others.
- Knowledge of grouping, syntactic parsing, and semantic parsing
- Working understanding of CI/CD pipelines is required.