Full Stack/Machine Learning Engineers

Hire Full Stack and Machine Learning Engineers with Works

Full-stack development is a complex task that involves 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 role of a remote full-stack developer can be challenging as they have to oversee database connections, debug the website or application, and more. In addition, Machine Learning Engineers are highly skilled programmers who specialize in researching, designing, and developing autonomous software for constructing prediction models. As a Machine Learning Engineer, you will be responsible for analyzing and manipulating data, testing and optimizing the learning process. If you have a passion for data, automation, and algorithms, then a Full Stack/Machine Learning Engineer role may be the perfect fit for you. You will be required to work with a large amount of data, create algorithms to analyze and interpret it, and automate the process to achieve maximum efficiency.

What Job Opportunities Are Available for Full Stack and Machine Learning Engineers?

Full-stack developers are in high demand due to their ability to handle a wider range of projects than traditional programmers. This enables them to save companies costs by taking the place of multiple specialists. Moreover, full-stack developers are skilled in a variety of stacks such as MEAN and LAMP, enabling them to meet the specific needs of a project. Similarly, the demand for Machine Learning (ML) engineers has been on the rise across sectors, presenting career security and diverse opportunities. According to market research firm IDC, the worldwide Artificial Intelligence (AI) industry is projected to be worth over $500 billion by 2024, indicating that Full Stack/Machine Learning career opportunities are promising. This is due to the increasing dependence on technology and the internet, which has created a growing need for these professionals. With this in mind, it is apparent that Full Stack/Machine Learning developers have a bright future, making it an ideal time to acquire these skills.

What Are the Roles and Responsibilities of Full Stack and Machine Learning Engineers?

Full-stack developers are in high demand due to their ability to develop both the frontend and backend of web and mobile applications. They can create visually appealing web applications that help businesses grow by attracting new customers online. They can also enhance the system’s functionality by writing the necessary code and hosting the website’s database on a server. Additionally, Full Stack/Machine Learning developers can switch seamlessly between frontend and backend development as per the project’s requirements. The key roles and responsibilities of Full Stack/Machine Learning engineers include designing and developing user interfaces, creating high-performing web applications, optimizing web applications for speed and scalability, implementing security and data protection protocols, and developing and implementing software solutions.

  • To create an AI-powered service, it is necessary to build a backend infrastructure, data pipelines, and/or machine learning models.
  • Our focus is on model ranking to automate and enhance modelling procedures.
  • Collaborate in developing innovative features that tackle complex data management problems. Learn more about addressing complex data management issues.
  • The machine learning models will be sent to end-users for testing.
  • Apply computer science fundamentals like data structures, algorithms, and machine learning in building exceptional ML models.
  • Creating a frontend architecture that can scale.
  • Supporting software design and development.
  • The development of full-stack software requires clean code.
  • Create and manage operational databases and servers.
  • Perform regular testing and debugging of the program to guarantee cross-platform optimization and compatibility.
  • Ensure applications remain responsive.
  • Collaborate with graphic designers to translate designs into visual components.
  • Develop APIs that facilitate interaction between computer programs.
  • Supporting the end-to-end development of a project.

What is the process for becoming a Full Stack/Machine Learning engineer?

Achieving success as a Full Stack/Machine Learning engineer is challenging, but possible. Whether you are an experienced IT professional or a coding enthusiast, acquiring the appropriate qualifications and specializations in the field can help you secure a highly-paid remote Full Stack/Machine Learning engineer position at your desired company. Being a Full Stack/Machine Learning engineer involves multiple aspects of both front-end and back-end technology, and a strong understanding of the entire application development process sets a solid foundation for success. Understanding object-oriented programming, HTML, CSS, and JavaScript is essential for a good Full Stack/Machine Learning engineer. Having a Bachelor’s or Master’s degree in Computer Science or comparable experience can enhance eligibility for a majority of remote Full Stack/Machine Learning engineer positions. Additionally, staying informed on the latest technologies is critical, so reading and continuous learning should be a regular part of a Full Stack/Machine Learning engineer’s education. With the basics of applying for a remote Full Stack/Machine Learning engineer job detailed, let us share the required skills to excel in the position.

Requirements for becoming a Full Stack/Machine Learning engineer

The following skills are necessary to reach the end goal of becoming a proficient Full Stack/Machine Learning engineer:


    As the foundation for web page creation, HTML and CSS are essential programming languages for front-end development. Thus, they are the initial steps for any aspiring full stack developer. To enhance their knowledge, many popular frameworks such as Bootstrap are utilized for generating HTML and CSS code for various components, including buttons and forms. Therefore, it is highly advised that any full stack developer has a grasp of such frameworks after mastering HTML and CSS.
  2. User Interface and User Experience

    UX or User Experience is a broad term that encompasses the overall experience a user has when interacting with an application or website. It includes everything from the placement of text, images, videos, and buttons, to how users interact with the User Interface (UI) or the visual components of an application or website. Making effective UI and UX design decisions that complement one another is critical for a full-stack developer to provide an optimal user experience. Good UI design is a priority, but not at the expense of a good user experience.
  3. Information Science

    A strong foundational knowledge of essential data science principles is crucial for machine learning engineers. This includes proficiency in programming languages like Python, SQL, and Java; the ability to perform hypothesis testing and data modelling; a sound understanding of mathematics, probability, and statistics, including Naive Bayes classifiers, conditional probability, likelihood, Bayes rule, Bayes nets, Hidden Markov Models, etc.; and the capability to develop an evaluation strategy for predictive models and algorithms.
  4. JavaScript

    JavaScript proficiency is an essential skill for both Full Stack and Machine Learning engineers. It is used in both the frontend and backend of applications and offers a wide range of capabilities. Object-Oriented Programming (OOP) in JavaScript involves using classes and objects, enabling developers to enhance the functionality of HTML and CSS-based web pages. JavaScript is an adaptable and powerful programming language, making it a valuable asset for any engineer specialising in web development.
  5. Frameworks and Backend Programming Languages

    Currently, developers have access to a wide range of backend programming languages. Once a programmer has mastered one language, transitioning to a different backend technology becomes a relatively straightforward task. Java, PHP, and Python are among the most commonly used backend languages, with Django, Express.js, Flask, and Laravel being some of the most popularly implemented frameworks. However, numerous other languages and frameworks are available for backend development.
  6. Other Skills

    Machine Learning Engineers are skilled in a variety of complex capabilities, including Neural Network Design, Deep Learning, Dynamic Programming, Natural Language Processing, Audio and Video Processing, Reinforcement Learning, Signal Processing, and Machine Learning Algorithm Optimisation. All of these proficiencies are necessary for a successful career in Machine Learning Engineering.
  7. Databases

    Databases are a crucial component in the functionality of computer programs, acting as a central repository for all necessary data required for the successful operation of the program. Therefore, Full Stack developers require a comprehensive understanding of database management systems (DBMS) as they may frequently be responsible for retrieving and delivering data from and to the database.

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

Remaining productive and consistent as a Full Stack/Machine Learning engineer necessitates staying current with the latest industry advancements and consistently improving one’s skillset. To achieve this, two critical steps should be taken. Firstly, it is advantageous to seek guidance from an experienced mentor to learn new techniques through practice. Secondly, one should strive to enhance their analytical, computer programming, artificial intelligence, and machine learning skills. At Works, we hire top developers from all over the world for remote Full Stack/Machine Learning engineer positions to ensure that such assistance is readily available. Moreover, taking on the most recent technological and commercial challenges is an excellent way to accelerate career growth. Lastly, joining the world’s largest developer network can provide access to numerous remote Full Stack/Machine Learning engineer jobs that offer competitive pay and advancement opportunities.

Job Overview

Job Duties

  • Develop a high-scale infrastructure system in addition to a data analytics pipeline.
  • Work collaboratively with engineers, data scientists, and designers to conceptualize and deliver new product features.
  • Conduct research and employ ML algorithms and tools to develop ML systems.
  • Examine complex and extensive datasets to obtain valuable insights.
  • Lead the creation of production-quality code.
  • Propose ideas for creating exceptional user experiences with straightforward and visually appealing interfaces.
  • Incorporate industry best practices and recommendations to enhance the existing machine learning infrastructure.
  • Collaborate in an interdisciplinary environment


  • A degree in Engineering, Computer Science, or Mathematics at the Bachelor’s or Master’s level (or comparable experience) is necessary.
  • 3+ years of developing machine learning systems for enterprise-grade applications (exceptions may apply for developers with outstanding expertise)
  • Proficiency in advanced mathematics, statistics, and related courses such as linear algebra, calculus, and Bayesian statistics is essential.
  • Familiarity with one or more programming languages, such as Clojure, Go/Golang, and Python 3.x
  • Comprehension of traditional SQL databases as well as modern graph database systems like Datomic
  • Proficiency in DevOps, infrastructure, and continuous integration principles is necessary.
  • Familiarity with web frameworks and ORM
  • Thorough understanding of JavaScript and the browser ecosystem, including event loops and related concepts.
  • Understanding of any modern JavaScript framework, such as React
  • Expertise in building complex systems
  • Proficiency in English is necessary for effective communication.
  • Work full-time (40 hours a week) with a 4-hour overlap with US time zones.

Desirable skills

  • Understanding of data processing, AI/ML, and NLP
  • Knowledge of TDD or BDD. Experience working with http/s, APIs, REST, and JSON
  • Exceptional critical thinking and problem-solving skills
  • Superb communication and organizational skills

Popular Questions

Can't find what you're looking for?
Visit our help center
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.