Full Stack/Machine Learning Engineers

Hire Full Stack/Machine Learning Engineers

Full-stack development is the process of designing and creating both the front-end and back-end features of a web application. This requires extensive programming skills and a deep understanding of the entire web development process. The job of a remote full-stack developer can be challenging due to the need to oversee database connection, debug the website or application, and more. Additionally, Machine Learning Engineers are highly skilled programmers that specialise in the research, design, and development of autonomous software to construct prediction models. As a Machine Learning Engineer, you will be responsible for analysing and manipulating data, testing, and optimising the learning process. If you are passionate about data, automation, and algorithms, a Full Stack/Machine Learning Engineer role may be the perfect fit for you. You will be expected to work with large amounts of data, design algorithms to analyse and interpret it, and automate the process for maximum efficiency.

What are the job opportunities for Full Stack/Machine Learning engineers?

The demand for full-stack developers is on the rise due to their ability to manage a wider scope of projects than traditional programmers. They can also help save companies money by taking the place of multiple specialists. Additionally, full-stack developers are knowledgeable in a range of stacks such as MEAN and LAMP, which allows them to meet the specific requirements of a project. Furthermore, the demand for Machine Learning (ML) engineers has been increasing across industries, providing career security and a variety of prospects. According to market research firm IDC, the global Artificial Intelligence (AI) industry is expected to be worth over $500 billion by 2024. This indicates that positions in Full Stack/Machine Learning have a promising future. This is due to the growing reliance on technology and the internet, which has created an increasing need for such professionals. Taking all this into consideration, it is clear that Full Stack/Machine Learning developers have a bright outlook, making it an opportune time to acquire this skill.

What are the duties and obligations of a Full Stack/Machine Learning engineer?

Full-stack developers are highly sought-after professionals who possess the skills and capabilities to develop both the frontend and backend of mobile and web applications. They are able to create visually stunning web applications that will help your business grow by aiding in the acquisition of new customers from the online realm. They can also improve the system’s functionality by writing the necessary code and hosting your website’s database on the server. Furthermore, Full Stack/Machine Learning developers are capable of switching between frontend and backend development as needed for the project. The primary responsibilities of a Full Stack/Machine Learning engineer include designing and developing user interfaces, developing web applications with optimal performance, optimising web applications for maximum speed and scalability, implementing security and data protection protocols, and developing and deploying software solutions.

  • For an AI-powered service, backend infrastructure, data pipelines, and/or machine learning models will be created.
  • We’re focusing on model ranking in order to automate and improve modelling procedures.
  • Contribute to the creation of novel features that address complex data management issues.
  • Machine learning models will be sent to end users, and testing will take place.
  • Utilise computer science essentials such as data structures, algorithms, and machine learning to create outstanding ML models.
  • Developing a front-end architecture that is scalable.
  • Assisting with the design and development of software.
  • Clean code is required for full-stack software development.
  • Make operational databases and servers.
  • Regularly test and debug the program to ensure cross-platform optimisation and compatibility.
  • Maintain the responsiveness of the applications.
  • Work with graphic designers to transform designs into visual components.
  • Create application programming interfaces (APIs) that enable computer programs to interact with one another.
  • Assisting in the creation of a project from start to finish.

How does one go about becoming a Full Stack/Machine Learning engineer?

Becoming a successful Full Stack/Machine Learning engineer is no easy feat, but it is achievable. Whether you are an experienced IT professional or a coding enthusiast, you must acquire the necessary qualifications and specialise in the field to secure a highly-paid remote Full Stack/Machine Learning engineer role at the company of your choice. Full Stack/Machine Learning engineering is a multi-faceted field, so you should become familiar with all aspects of front-end and back-end technology. Moreover, having a strong understanding of the entire application development process will help you build a solid foundation in the domain. To start with, a good Full Stack/Machine Learning engineer should have a command over object-oriented programming, HTML, CSS, and JavaScript. To be eligible for the majority of remote Full Stack/Machine Learning engineer positions, having a Bachelor’s or Master’s degree in Computer Science, or equivalent experience, is beneficial. Furthermore, a remote Full Stack/Machine Learning engineer‘s education is never complete, as one must stay up-to-date with the latest technologies. Therefore, it is important to make sure you read as much as you can to stay informed. Now that you have acquired the basics of applying for a remote Full Stack/Machine Learning engineer job, let us provide you with the skills needed to excel in the role.

Qualifications for becoming a Full Stack/Machine Learning engineer

The following are the abilities needed to get to the ultimate objective of being a professional Full Stack/Machine Learning engineer:


    Front-end development relies heavily on HTML and CSS as the foundations of web page creation. Therefore, these two programming languages are the first steps for any aspiring full stack developer. In order to further one’s knowledge, many popular frameworks such as Bootstrap are used to generate HTML and CSS code for various components including buttons and forms. Therefore, it is highly recommended that any full stack developer have a working understanding of such frameworks once they have mastered HTML and CSS.
  2. User Interface and User Experience

    User Experience (UX) is an umbrella term that describes the overall experience a user has when interacting with an application or website. It encompasses everything from the placement of buttons, images, videos, and text, to how people interact with the User Interface (UI), the visual components of an application or website. It is important that a full-stack developer be able to make both UI and UX design decisions that work together to provide an optimal user experience. A pleasant user interface should be a priority, but not if it comes at the cost of a satisfactory user experience.
  3. Information science

    Having a strong grasp of fundamental data science principles is crucial for machine learning engineers. This includes being knowledgeable in programming languages such as Python, SQL, and Java; being able to conduct hypothesis testing and data modelling; having proficiency in mathematics, probability, and statistics (including Naive Bayes classifiers, conditional probability, likelihood, Bayes rule, Bayes nets, Hidden Markov Models, etc.); and having the capacity to devise an evaluation strategy for predictive models and algorithms.
  4. JavaScript

    JavaScript is an essential skill for a Full Stack or Machine Learning engineer to possess. It is utilised in both the frontend and backend of applications, allowing for a wide range of capabilities. Object-Oriented Programming (OOP) in JavaScript refers to the utilisation of classes and objects, which are two powerful tools that can be used to extend the functionality of HTML and CSS-based web pages. JavaScript is a versatile and powerful programming language, making it a valuable asset for any engineer specialising in web development.
  5. Frameworks and backend programming languages

    In the present day, there is a wide selection of backend programming languages for developers to choose from. Once a programmer has acquired mastery over one language, the transition to any other backend technology will be a comparatively simple endeavour. Java, PHP, and Python are some of the most popularly used backend languages, with Django, Express.js, Flask, and Laravel being some of the most commonly implemented frameworks. There are, however, numerous other languages and frameworks which can be utilised in backend development.
  6. Other abilities

    Machine Learning Engineers are adept in a variety of complex skills, such as Deep Learning, Dynamic Programming, Neural Network Design, Natural Language Processing, Audio and Video Processing, Reinforcement Learning, Signal Processing, and Machine Learning Algorithm Optimisation. All of these skills are essential for successful Machine Learning Engineering.
  7. Databases

    Databases play an integral role in the functionality of computer programs, acting as a central repository for all the data required to ensure the successful operation of the program. As such, full-stack developers need to have a good understanding of database management systems (DBMS) since they will frequently be responsible for both retrieving and delivering data from and to the database.

How can I get work as a remote Full Stack/Machine Learning engineer?

As a Full Stack/Machine Learning engineer, it is essential to stay up to date with the latest industry advancements and continuously develop one’s skills in order to remain productive and consistent. In order to achieve this, two key steps should be taken. Firstly, it is beneficial to seek assistance from an experienced and knowledgeable mentor in order to learn new techniques through practice. Secondly, it is necessary to improve one’s analytical, computer programming, artificial intelligence, and machine learning skills. To ensure that such help is available, Works hires the top developers in the world for remote Full Stack/Machine Learning engineer positions. Furthermore, taking on the most current technological and commercial challenges is a great way to accelerate one’s career growth. Finally, joining the world’s largest developer network can provide access to many remote Full Stack/Machine Learning engineer jobs with competitive pay and advancement opportunities.

Job Description

Job responsibilities

  • Create a large-scale system infrastructure as well as a data analytics pipeline.
  • Collaborate with designers, data scientists, and engineers to create and produce new product features.
  • Develop ML systems by researching and implementing ML algorithms and tools.
  • Analyse vast and complicated datasets to get useful insights.
  • Take charge of producing production-quality code.
  • Share your ideas for creating outstanding user experiences with simple and clean interfaces.
  • Implement best practices and recommendations to improve the current machine learning infrastructure.
  • Work in an interdisciplinary setting


  • Bachelor’s or Master’s degree in Engineering, Computer Science, or Mathematics is required (or equivalent experience)
  • 3+ years of enterprise-grade, machine learning-based system development (rare exceptions for highly skilled developers)
  • Advanced arithmetic, statistics, and related courses such as linear algebra, calculus, and Bayesian statistics are required.
  • Knowledge of one or more programming languages, such as Go/Golang, Clojure, and Python 3.x
  • Understanding of classic SQL databases as well as contemporary graph database systems such as Datomic
  • Knowledge of DevOps, infrastructure, and continuous integration principles is required.
  • Knowledge of web frameworks and ORM
  • Strong knowledge of JavaScript and the browser ecology, event loops, and so forth.
  • Knowledge of any current JavaScript framework, such as React
  • Experience in developing complicated systems
  • English fluency is required for good communication.
  • Work full-time (40 hours per week) with a 4-hour overlap with US time zones.

Preferred skills

  • Knowledge of data processing, AI/ML, and NLP
  • TDD or BDD knowledge Experience dealing with http/s, APIs, REST, and JSON
  • Excellent critical thinking and problem-solving abilities
  • Outstanding communication and organising abilities


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What makes Works Full Stack/Machine Learning Engineers different?
At Works, we maintain a high success rate of more than 98% by thoroughly vetting through the applicants who apply to be our Full Stack/Machine Learning Engineer. To ensure that we connect you with professional Full Stack/Machine Learning Engineers of the highest expertise, we only pick the top 1% of applicants to apply to be part of our talent pool. You'll get to work with top Full Stack/Machine Learning Engineers to understand your business goals, technical requirements and team dynamics.