Remote ML/NLP engineer jobs

We, at Turing, are looking for ML/NLP engineers who will make use of NLP techniques, ML algorithms, statistical analysis, and text representation techniques to help extract valuable information from large datasets. Here's your chance to accelerate your career while working with top U.S. companies.

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Job description

Job responsibilities

  • Define appropriate datasets for training the model and evaluating test results
  • Design, develop, and maintain natural language processing (NLP) systems
  • Develop and integrate ML/NLP models into existing applications
  • Evaluate existing models to identify areas for improvement
  • Develop and maintain code for data analysis
  • Create software libraries and tools to facilitate model development

Minimum requirements

  • Bachelor’s/Master’s Degree in Computer Science (or equivalent experience)
  • 3+ years of experience as an ML/NLP engineer (rare exceptions for skilled devs)
  • Experience in sentiment analysis, text classification, and classification algos
  • Proficiency in programming languages such as Python, Java, C++, etc.
  • Experience with machine learning (ML) tools and libraries such as NLTK, spaCy, Gensim, etc.
  • Familiarity with deep learning libraries and frameworks such as TensorFlow, Keras, PyTorch, etc.
  • Knowledge of natural language understanding (NLU) techniques and applications
  • Fluency in English to collaborate with engineering managers
  • Work full-time (40 hours/week) with a 4-hour overlap with US time zones

Preferred skills

  • Knowledge of source control systems (Git, merging, branching)
  • Experience in Unix/Linux, including basic commands and scripting
  • Familiarity with big data frameworks such as Spark, Hadoop, etc
  • Experience with clustering, syntactic parsing, semantic parsing
  • Ability to communicate complex technical concepts to a non-technical audience
  • Ability to work independently and collaboratively in a team environment

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How to become an ML/NLP engineer ?

The advancement of ML and NLP makes these technologies a promising professional path. According to research by Indeed, ML/NLP engineer jobs rank the top in terms of compensation, job growth, and overall demand. Professionals with machine learning skills are in high demand and in short supply, which helps to explain why the profession is so valuable.

ML/NLP necessitates working knowledge of programming, statistics, and data analysis. It can even include leadership roles in automation or analytics environments that employ data science, big data analysis, AI integration, and other techniques.

What is the scope in ML/NLP engineering

According to GlobeNewswire, the global ML market size is expected to reach at a compound annual growth rate (CAGR) of 38.1% from 2021 to 2030. In the year 2021, it was valued at USD 14.91 billion. This defines the scope of ML-related jobs. What about natural language processing?

Advancements in processing power have hastened the evolution of NLP. Industry experts believe its implementation will remain one of the top big data issues in the coming years. These reports clearly show the scope of ML/NLP engineer jobs in the future.

Are you tempted to apply for remote ML/NLP engineer jobs? Let us now delve into the details to learn more about the various aspects.

What are the roles and responsibilities of an ML/NLP engineer?

An ML/NLP engineer will break down language into smaller, more basic structures, seeks to understand the relationships between them, and examines how the structural elements interact to form meaning.

As an ML/NLP engineer, you will be responsible for leveraging data to train models. You will then have to use the models to automate tasks, such as picture categorization, speech recognition, text classification, and market forecasting. That's not all, though. You will also need to develop devices and systems that can comprehend human speech.

How to become an ML/NLP engineer

The first and most important step is to learn how to code in Python and R. You can then enroll in a machine learning course. Udemy, Coursera, etc., provide a variety of such courses. Once you've mastered the fundamentals, undertake a machine learning project. There is no substitute for real-world experience! Begin learning how to collect the appropriate data at the same time.

Join online machine learning groups or even enter a contest or hackathon. You can use this as an opportunity to put your abilities to the test and meet new people who can help you advance your career. Once you complete your degree, you can apply for machine learning internships and jobs.

You will be assessed on math, statistics, and probability knowledge during the selection process. You will also be evaluated in crucial areas such as NLP fundamental approaches. Make sure you do your homework and apply to jobs with an attractive ML/NLP developer resume.

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Skills required to become an ML/NLP engineer

Learning the necessary skills is the first step toward gaining remote ML/NLP engineer jobs. Let's have a look at them right now.

1. ML algorithms

Knowledge of standard ML algorithms is vital. Supervised, unsupervised, and reinforcement algorithms are the three most prevalent forms. Naive Bayes classifier, k-means clustering, support vector machine, apriori algorithm, linear regression, logistic regression, decision trees, and random forests are some common ones.

2. Data modeling and evaluation

As an ML/NLP engineer, you should be able to model and evaluate data. Understanding the data's fundamental structure and looking for patterns is what data modeling entails. Additionally, you should be able to use the appropriate approach to evaluate data for example, regression, classification, clustering, dimension reduction, etc. Knowing the various techniques in order to properly contribute to data modeling and assessment is the key.

3. Neural networks

While it isn't necessary to be an expert in neural networks to be hired for ML/NLP engineer jobs, it is important to understand the principles. This can include feedforward neural networks, recurrent neural networks, convolutional neural networks, modular neural networks, radial basis function neural networks, etc.

4. NLP tools and techniques

You must have a good understanding of NLP techniques such as lemmatization, part-of-speech tagging, and sentiment analysis. These techniques are used to analyze and interpret the meaning of language and to identify patterns in text data.

NLP is built on the foundation of many diverse libraries. These libraries contain several functions that help computers understand natural language by breaking the text down into the basics, extracting key phrases, and deleting unnecessary words, among other things. Natural Language Toolkit is among the most widely used platforms for developing NLP applications.

5. Probability and statistics

Some models, such as n-gram language modeling, rely on "guessing" given conditions. You need to know probability and statistics as both will be used when handling and analyzing corpora.

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How to get remote ML/NLP engineer jobs

Machine learning is becoming more common and is now being employed in practically every sector, including healthcare, cybersecurity, and the automotive industry. Choosing to build a career in ML/NLP engineering is a fantastic path to take.

Turing has top ML/NLP engineer jobs that match your job goals. Enjoy the opportunity to work on complex technical and business problems to advance your career. Get full-time, long-term remote ML/NLP engineer jobs with excellent income and career growth prospects by joining a network of the world's best developers.

Why become an ML/NLP engineer at Turing?

Elite U.S. jobs
Elite U.S. jobs

Long-term opportunities to work for amazing, mission-driven U.S. companies with great compensation.

Career growth
Career growth

Work on challenging technical and business problems using cutting-edge technology to accelerate your career growth.

Exclusive developer community
Exclusive developer community

Join a worldwide community of elite software developers.

Once you join Turing, you’ll never have to apply for another job.
Once you join Turing, you’ll never have to apply for another job.

Turing's commitments are long-term and full-time. As one project draws to a close, our team gets to work identifying the next one for you in a matter of weeks.

Work from the comfort of your home
Work from the comfort of your home

Turing allows you to work according to your convenience. We have flexible working hours and you can work for top U.S. firms from the comfort of your home.

Great compensation
Great compensation

Working with top U.S. corporations, Turing developers make more than the standard market pay in most nations.

How much does Turing pay their ML/NLP engineers?

Every ML/NLP engineer at Turing gets a chance to fix their pricing. Turing will suggest compensation at which we are confident we can find a secure and long-term opportunity to level up your ML/NLP engineer career. Our recommendations are based on an analysis of current market conditions and client demand.

Frequently Asked Questions

Having the knowledge of data structures, semantic extraction, modeling, text representation like n-grams, sentiment analysis, bag of words, etc., is needed. You should be familiar with R, Java, Python and Machine Learning frameworks like PyTorch, Keras, along with the ability to write codes and design software architectures. If you are an expert in the skills mentioned above and want to work from the comfort of your home, sign up at Turing.

Having a thorough understanding of data structures, data modeling, and software architecture with the skill to write codes in Python, Java, and R is a must. It's vital to know Machine Learning frameworks like Keras or PyTorch and libraries like Scikit-learn.

We are a Palo Alto-based 'deep' jobs platform allowing talented software developers to work with top US firms from the comfort of their homes. We are led by Stanford alumni and successful A.I. entrepreneurs Jonathan Siddharth and Vijay Krishnan.

An NLP engineer's job is to design NLP applications and products, recognizing and using the right algorithms for particular NLP projects. They are responsible for executing statistical analysis of models, transforming Data Science prototypes, and various other tasks.

Yes, machine learning is a fulfilling career. The demand ML holds in the market amongst companies is commendable. Machine Learning enables engineers to determine various real-world problems encountered by predictive analysis. It is now more easier than ever to predict the victory or defeat of a product or a choice. If you are looking for a job as a Machine Learning engineer, explore exciting remote opportunities at

We, at Turing, hire remote developers for over 100 skills like React/Node, Python, Angular, Swift, React Native, Android, Java, Rails, Golang, PHP, Vue, among several others. We also hire engineers based on tech roles and seniority.

An ML engineer's work deals with designing Machine Learning systems, studying and transforming Data Science prototypes, examining and executing the right ML algorithms and tools. Their responsibility is to select the correct datasets and data representation process, extend current ML libraries and framework and deal with other various tasks.

Machine Learning is an in-demand career these days. Machine Learning engineers ensure that the models adopted by Data Scientists can examine vast amounts of data in real-time for acquiring accurate results. It's poised to keep rising in the upcoming days as every organization looks to keep growing digitally. If you want to work remotely for the top U.S. companies with a well-paid salary, sign up on

It is the combination of our core business model and values that makes us different from others. We provide full-time, long-term projects to remote developers whereas most of our competitors offer more freelance jobs.

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In a nutshell, Turing aims to make the world flat for opportunity. Turing is the brainchild of serial A.I. entrepreneurs Jonathan and Vijay, whose previous successfully-acquired AI firm was powered by exceptional remote talent. Also part of Turing’s band of innovators are high-profile investors, such as Facebook's first CTO (Adam D'Angelo), executives from Google, Amazon, Twitter, and Foundation Capital.

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