Remote machine learning engineer jobs

We, at Turing, are looking for talented machine learning engineers who can build the most optimized product features applying high-end ML modeling techniques. Join forces with the top 1% of ML engineers and grow with the best minds.

Find remote software jobs with hundreds of Turing clients

Job description

Job responsibilities

  • Building back-end infrastructure, data pipelines, and/or machine learning models for our AI-backed product
  • Build working ranking models and automate modeling pipelines
  • Implement new features solving complex data management problems
  • Deploy machine learning models to end-users and run experiments
  • Build great ML models using computer science fundamentals: data structures, algorithms, programming languages, distributed systems, and information retrieval

Minimum requirements

  • Bachelor’s/Master’s degree in Computer Science, Engineering, IT, or relevant field
  • 2+ years of experience in engineering and ML methods
  • In-depth understanding of applied machine learning algorithms, especially NLP, and statistics
  • Comfortable with data science as well as with the engineering required to bring your models to production
  • Experience in deploying models and algorithms in production
  • Experience with both SQL and NoSQL databases
  • Proficiency in Python programming
  • Good testing skills

Preferred skills

  • Experience with CI/CD (Jenkins in particular), DVC, model monitoring tools, MLOps in general
  • Knowledge of ML techniques: deep learning, reinforcement learning, classification, pattern recognition, etc.
  • Knowledge of recommendation systems, targeting systems, ranking systems, or similar techniques

Interested in this job?

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How to become an ML engineer?

Machine Learning Engineer jobs demand proficient programmers adept at researching, developing, and designing autonomous software for predictive model automation. These engineers specialize in crafting AI systems that leverage extensive datasets to devise algorithms capable of learning and predicting outcomes. Their responsibilities include scrutinizing, analyzing, and structuring data to aid in the development of high-performance machine learning models, conducting tests, and optimizing the learning process.

Machine learning is the appropriate career choice for you if you're interested in data, automation, and algorithms. Moving vast volumes of raw data, building algorithms to process that data, and then automating the process for optimization will occupy your days.

Another reason why machine learning is such an exciting field to work in? Within the industry, there are numerous career routes to choose from. You can work as a Machine Learning Engineer, Data Scientist, NLP Scientist, Business Intelligence Developer, or Human-Centered Machine Learning Designer if you have a background in machine learning.

In addition, creating a concise yet comprehensive machine learning engineer resume is crucial. It's essential for showcasing your potential effectively to prospective employers.

What is the scope of ML engineering?

Because ML engineer roles are in high demand across industries, they provide career security and a variety of opportunities. From 2018 to 2027, the worldwide AI and ML industry is predicted to grow at a steady rate, according to several studies. The global AI sector will be worth more than half a trillion dollars by 2024, according to market research firm IDC.

The growing number of AI startups and renewed interest in the subject among existing firms is a result of the global demand for AI/ML technologies and applications. The number of AI startup acquisitions has steadily increased since 2010, approximately quadrupling between 2015 and 2018. Gains in AI startup acquisitions have paralleled increases in AI startup funding, which has increased from over a billion dollars in 2013 to 8.5 billion dollars in the first quarter of 2020. Because high-skilled ML engineers are continually in demand across industries, remote ML engineer job postings are rarely unfilled.

What are the roles and responsibilities of an ML engineer?

On the team, ML engineer’s responsibilities include a variety of tasks, such as -

  • For an AI-powered solution, you'll be designing backend infrastructure, data pipelines, and/or machine learning models.
  • Working on ranking models to automate and develop modeling pipelines.
  • Contribute to the implementation of new features that address challenging data management issues.
  • End-users will be given machine learning models to utilize, and tests will be conducted.
  • Using computer science essentials such as data structures, algorithms, and machine learning, create fantastic ML models.
  • Programming languages, distributed systems, and information retrieval are all topics covered in this course.

Aside from these, an ML engineer’s role and responsibilities may entail more. Because this industry is still in its early stages and many things remain undiscovered, each organization has its unique set of productive automation approaches.

As a result, ML engineer jobs at IT companies may cover a variety of extra responsibilities, including:

  • Collaboration between data scientists and business analysts.
  • Infrastructure automation.
  • Creating APIs via converting machine learning models.
  • Putting AI/ML models to the test and deploying them.
  • Development of minimum viable products using machine learning.
  • Using AI to deliver new talents to businesses.

How to become an ML engineer?

To get machine learning engineer jobs, you'll need to have a few prerequisites. In general, this function is in charge of designing machine learning applications and systems, which includes analyzing and organizing data, running tests and experiments, and generally monitoring and optimizing the learning process to develop high-performing ML systems.

As an ML Engineer, you'll be responsible for applying algorithms to various codebases, therefore previous software development expertise is ideal for this position. Essentially, the right mix of math, statistics, and web programming will provide you with the necessary background — once you understand these ideas, you'll be ready to apply for ML Engineering employment.

Interested in remote ML engineer jobs?

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Skills required to become an ML engineer

The field of ML engineer jobs is relatively new and quickly evolving. As a result, there is no single skill set that can be used to become an ML engineer. There are a multitude of ways to break into the sector depending on your educational background, technical skills, and areas of interest.

Some of the abilities you must acquire if you want to get  ML engineer jobs are:

1. Skills in software engineering

Writing algorithms that can search, sort, and optimize; familiarity with approximate algorithms; understanding data structures such as stacks, queues, graphs, trees, and multi-dimensional arrays; understanding computability and complexity; and knowledge of computer architecture such as memory, clusters, bandwidth, deadlocks, and cache are just a few of the computer science fundamentals that machine learning engineers rely on.

2. Skills in data science

Familiarity with programming languages such as Python, SQL, and Java; hypothesis testing; data modeling; proficiency in mathematics, probability, and statistics (such as Naive Bayes classifiers, conditional probability, likelihood, Bayes rule, and Bayes nets, Hidden Markov Models, and so on); and the ability to develop an evaluation strategy for predictive models and algorithms are just a few of the data science fundamentals that machine learning engineers rely on.

3. Additional skills in machine learning

Deep learning, dynamic programming, neural network designs, natural language processing, audio, and video processing, reinforcement learning, sophisticated signal processing techniques and the optimization of machine learning algorithms are all skills that many machine learning engineers have.

4. Security is a key task for AI/ML systems

as it is for any other software solution. While substantial data preparation is required for Machine Learning models, data access should be limited to just authorized personnel and applications. At all costs, data security is a skill that must be learned.

5. Experience with real-world projects

Another crucial aspect of becoming an ML engineer is recognizing when and how to apply your technical expertise to practical tasks and assignments. Completing an AI/ML development project from beginning to end and documenting it in your portfolio will help you pitch your skills and knowledge to potential employers, allowing you to land those remote ML engineer jobs you've always desired.

6. Communication skills

Machine learning engineers frequently collaborate with data scientists and analysts, software engineers, research scientists, marketing teams, and product teams, therefore the ability to accurately explain project goals, timetables, and expectations to stakeholders is an essential skill.

7. Possesses problem-solving abilities

Both data scientists and software engineers need problem-solving skills, and machine learning engineers require them. Because machine learning focuses on solving problems in real-time, the ability to think critically and creatively about problems and generate solutions is a prerequisite.

8. Domain expertise

Machine learning engineers must understand both the needs of the business and the types of problems that their designs are solving to create self-running software and optimize solutions utilized by businesses and customers. Without domain knowledge, a machine learning engineer's recommendations may be inaccurate, their work may overlook useful features, and evaluating a model may be challenging.

These skills, combined with continuous learning and practice of machine learning interview questions, will help aspiring developers become proficient in machine learning.

Interested in remote ML engineer jobs?

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How to get remote ML engineer jobs?

To advance in the constantly evolving field of machine learning, ML engineers must remain diligent in keeping pace with industry advancements and continuously enhance their skills. Excelling in this domain requires consistent adherence to best practices. Two key considerations for progress include seeking guidance from experienced mentors to acquire new skills effectively during practice sessions and refining analytical, programming, and AI/ML skills. Ensuring access to support is imperative for ML engineers to thrive in their roles.

Turing has the best ML engineer jobs that fit your AI/ML engineering career goals. Get full-time, long-term remote ML engineer jobs with greater pay and faster career progression by joining a network of the world's greatest developers.

Why become an ML engineer at Turing?

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

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

Join a worldwide community of elite software developers.

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.

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

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

How much does Turing pay their ML engineers?

Every ML engineer at Turing has the freedom to select his/her rate. Turing, on the other hand, will recommend a wage at which we are confident we can offer you a rewarding and long-term opportunity. Our remote ML engineer jobs recommendations are based on our market analysis and demand from our most prestigious clients.

Frequently Asked Questions

Turing is an AGI infrastructure company specializing in post-training large language models (LLMs) to enhance advanced reasoning, problem-solving, and cognitive tasks. Founded in 2018, Turing leverages the expertise of its globally distributed technical, business, and research experts to help Fortune 500 companies deploy customized AI solutions that transform operations and accelerate growth. As a leader in the AGI ecosystem, Turing partners with top AI labs and enterprises to deliver cutting-edge innovations in generative AI, making it a critical player in shaping the future of artificial intelligence.

After uploading your resume, you will have to go through the three tests -- seniority assessment, tech stack test, and live coding challenge. Once you clear these tests, you are eligible to apply to a wide range of jobs available based on your skills.

No, you don't need to pay any taxes in the U.S. However, you might need to pay taxes according to your country’s tax laws. Also, your bank might charge you a small amount as a transaction fee.

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.

Communication is crucial for success while working with American clients. We prefer candidates with a B1 level of English i.e. those who have the necessary fluency to communicate without effort with our clients and native speakers.

Currently, we have openings only for the developers because of the volume of job demands from our clients. But in the future, we might expand to other roles too. Do check out our careers page periodically to see if we could offer a position that suits your skills and experience.

Our unique differentiation lies in the combination of our core business model and values. To advance AGI, Turing offers temporary contract opportunities. Most AI Consultant contracts last up to 3 months, with the possibility of monthly extensions—subject to your interest, availability, and client demand—up to a maximum of 10 continuous months. For our Turing Intelligence business, we provide full-time, long-term project engagements.

No, the service is absolutely free for software developers who sign up.

Ideally, a remote developer needs to have at least 3 years of relevant experience to get hired by Turing, but at the same time, we don't say no to exceptional developers. Take our test to find out if we could offer something exciting for you.

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Leadership

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.

Equal Opportunity Policy

Turing is an equal opportunity employer. Turing prohibits discrimination and harassment of any type and affords equal employment opportunities to employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, age, disability status, protected veteran status, or any other characteristic protected by law.

Explore remote developer jobs

briefcase
Senior Fullstack Engineer - Backend Heavy

Job Overview

We are seeking a highly skilled Senior Full Stack Engineer with a strong focus on backend architecture and expertise in artificial intelligence (AI) to join our dynamic team. The ideal candidate will have 5-7 years of experience in designing, developing, and maintaining robust  full-stack applications, with deep expertise in Python, data structures, and backend database interactions, API design, authentication systems, and AI-driven technologies. You will play a critical role in architecting scalable, secure, and high-performance systems, integrating AI capabilities such as Retrieval-Augmented Generation (RAG), vector databases, large language model (LLM) APIs, and more to power our innovative solutions.

Key Responsibilities

● Design and implement scalable backend architectures for full-stack applications using Python and related frameworks (e.g., Django, Flask, FastAPI).
●  Develop and optimize complex data structures and algorithms to ensure efficient data processing and storage.
●  Architect and manage interactions with relational and non-relational databases (e.g., PostgreSQL, MongoDB) and vector databases (e.g., Pinecone, Weaviate) to support application and AI functionality.
●  Design, develop, and maintain secure, efficient, and well-documented RESTful APIs and GraphQL endpoints, integrating AI-driven features such as RAG and LLM APIs.
●  Implement robust authentication and authorization mechanisms (e.g., OAuth, JWT, SSO) to ensure system security.
●  Collaborate with frontend developers to integrate backend services and AI-powered features with user interfaces, ensuring seamless end-to-end functionality.
●  Develop and integrate AI solutions, including RAG pipelines, LLM API integrations (e.g., OpenAI, Hugging Face), and vector database queries for enhanced data retrieval and processing.
●  Perform data labeling, classification, and model training for AI-driven applications, ensuring high-quality datasets and model performance.
● Conduct red teaming exercises to evaluate and improve the security and robustness of AI systems and backend infrastructure.
●  Write clean, maintainable, and testable code, adhering to best practices and coding standards.
●  Design, implement, and maintain CI/CD pipelines to automate testing, deployment, and monitoring of backend and AI-driven applications, ensuring rapid and reliable delivery.
●  Optimize application and AI model performance, troubleshoot issues, and ensure high availability and reliability.
●  Mentor junior engineers, conduct code reviews, and contribute to architectural decisions, including AI strategy.
●  Stay updated on industry trends, emerging AI technologies, and backend development practices to recommend improvements and innovations.

Qualifications

● Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience).
●  5-7 years of professional experience in full-stack development, with a strong emphasis on backend systems.
●  Expertise in Python and its ecosystems (e.g., Django, Flask, FastAPI) for building scalable applications.
●  Strong understanding of data structures, algorithms, and software design principles.
●  Extensive experience with database management, including SQL (e.g., PostgreSQL, MySQL), NoSQL (e.g., MongoDB, Redis), and vector databases (e.g., FAISS, Quadrant, Pinecone, Weaviate).  
●  Solid understanding of embeddings and how these work with vector databases
●  Proven ability to design and implement secure APIs (REST, GraphQL) and authentication systems (OAuth, JWT, etc.).
●  Experience with AI technologies, including RAG, LLM APIs (e.g., OpenAI, Hugging Face), vector databases, and model training/classification.
●  Familiarity with data labeling, preprocessing, and red teaming for AI model development and evaluation.
●  Knowledge of frontend technologies (e.g., JavaScript, React, Vue.js) to collaborate effectively with frontend teams.
●  Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (e.g., Docker, Kubernetes) is a plus.
●  Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
●  Excellent communication skills and a passion for mentoring and knowledge sharing.


Preffered Skills

● Experience with microservices architecture and distributed systems.
●  Knowledge of CI/CD pipelines and DevOps practices.
●  Familiarity with testing frameworks (e.g., pytest, unittest) and writing automated tests for both backend and AI components.
●  Understanding of AI security best practices, including red teaming and compliance standards (e.g., GDPR, OWASP).
●  Good understanding of AI techniques (e.g. (CoT, reasoning, MCP)
●  Contributions to open-source AI or backend projects or a strong portfolio showcasing relevant work.
●  Experience with frameworks like LangChain, LlamaIndex, or similar for building AI driven applications.

Interview Process

  • 1-2 technical rounds with the client

Offer Details

  • Full-time contractor (no benefits)
  • Remote only, full-time dedication (40 hours/week)
  • Required 4-6 hours overlap with Pacific Timezone
  • Competitive compensation package.
  • Opportunities for professional growth and career development.
  • Dynamic and inclusive work environment focused on innovation and teamwork


-
11-50 employees
DjangoFlaskFastAPI+ 5
briefcase
Senior Fullstack Engineer - Frontend Heavy

Job Overview We are seeking a highly skilled Senior Full Stack Engineer with a strong focus on backend architecture and expertise in artificial intelligence (AI) to join our dynamic team. The ideal candidate will have 5-7 years of experience in designing, developing, and maintaining robust  full-stack applications, with deep expertise in Python, data structures, and backend database interactions, API design, authentication systems, and AI-driven technologies. You will play a critical role in architecting scalable, secure, and high-performance systems, integrating AI capabilities such as Retrieval-Augmented Generation (RAG), vector databases, large language model (LLM) APIs, and more to power our innovative solutions.  

Key Responsibilities

● Design and implement scalable backend architectures for full-stack applications using Python and related frameworks (e.g., Django, Flask, FastAPI).
●  Develop and optimize complex data structures and algorithms to ensure efficient data processing and storage.
●  Architect and manage interactions with relational and non-relational databases (e.g., PostgreSQL, MongoDB) and vector databases (e.g., Pinecone, Weaviate) to support application and AI functionality.
●  Design, develop, and maintain secure, efficient, and well-documented RESTful APIs and GraphQL endpoints, integrating AI-driven features such as RAG and LLM APIs.
●  Implement robust authentication and authorization mechanisms (e.g., OAuth, JWT, SSO) to ensure system security.
●  Collaborate with frontend developers to integrate backend services and AI-powered features with user interfaces, ensuring seamless end-to-end functionality.
●  Develop and integrate AI solutions, including RAG pipelines, LLM API integrations (e.g., OpenAI, Hugging Face), and vector database queries for enhanced data retrieval and processing.
●  Perform data labeling, classification, and model training for AI-driven applications, ensuring high-quality datasets and model performance.
● Conduct red teaming exercises to evaluate and improve the security and robustness of AI systems and backend infrastructure.
●  Write clean, maintainable, and testable code, adhering to best practices and coding standards.
●  Design, implement, and maintain CI/CD pipelines to automate testing, deployment, and monitoring of backend and AI-driven applications, ensuring rapid and reliable delivery.
●  Optimize application and AI model performance, troubleshoot issues, and ensure high availability and reliability.
●  Mentor junior engineers, conduct code reviews, and contribute to architectural decisions, including AI strategy.
●  Stay updated on industry trends, emerging AI technologies, and backend development practices to recommend improvements and innovations.

Qualifications

● Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience).
●  5-7 years of professional experience in full-stack development, with a strong emphasis on backend systems.
●  Familiarity in Python and its ecosystems (e.g., Django, Flask, FastAPI) for building scalable applications.
●  Strong understanding of data structures, algorithms, and software design principles.
●  Extensive experience with database management, including SQL (e.g., PostgreSQL, MySQL), NoSQL (e.g., MongoDB, Redis), and vector databases (e.g., FAISS, Quadrant, Pinecone, Weaviate).
●  Solid understanding of embeddings and how these work with vector databases
●  Proven ability to design and implement secure APIs (REST, GraphQL) and authentication systems (OAuth, JWT, etc.).
●  Experience with AI technologies, including RAG, LLM APIs (e.g., OpenAI, Hugging Face), vector databases, and model training/classification.
●  Familiarity with data labeling, preprocessing, and red teaming for AI model development and evaluation.
●  Expertise in  frontend technologies (e.g., JavaScript, React, Vue.js) to collaborate effectively with backend teams.
●  Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (e.g., Docker, Kubernetes) is a plus.
●  Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
●  Excellent communication skills and a passion for mentoring and knowledge sharing.

Preffered Skills

● Experience with microservices architecture and distributed systems.
●  Knowledge of CI/CD pipelines and DevOps practices.
●  Familiarity with testing frameworks (e.g., pytest, unittest) and writing automated tests for both backend and AI components.
●  Understanding of AI security best practices, including red teaming and compliance standards (e.g., GDPR, OWASP).
●  Good understanding of AI techniques (e.g. (CoT, reasoning, MCP)
●  Contributions to open-source AI or backend projects or a strong portfolio showcasing relevant work.
●  Experience with frameworks like LangChain, LlamaIndex, or similar for building AI driven applications.  

Interview Process

  • 1-2 technical rounds with the client

Offer Details

  • Full-time contractor (no benefits)
  • Remote only, full-time dedication (40 hours/week)
  • Required 4-6 hours overlap with Pacific Timezone
  • Competitive compensation package.
  • Opportunities for professional growth and career development.
  • Dynamic and inclusive work environment focused on innovation and teamwork
-
11-50 employees
ReactVue.jsAngular+ 5
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