Remote Machine Learning Jobs
Remote machine learning jobs are in active demand across the U.S., with remote-first firms and distributed engineering teams hiring for roles in tech, finance, healthcare, and AI research. Employers hiring remotely right now include CVS Health, Alvarez & Marsal, and Netflix. See the openings below and apply to the ones that match your experience.
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Showing 5 of 496+ Remote Machine Learning jobs
ABOUT GITHUB
GitHub is the world’s leading platform for agentic software development — powered by Copilot to build, scale, and deliver secure software. Over 180 million developers, including more than 90% of the Fortune 100 companies, use GitHub to collaborate, and more than 77,000 organisations have adopted GitHub Copilot.
LOCATIONS
In this role you can work from Remote, United States
Overview
GitHub is changing the way the world builds software and we want you to help build and secure GitHub. We're looking for an experienced machine learning engineer to help design, build and deploy agentic solutions, and to conduct ad-hoc analysis, as you help protect the home of all developers.
You will be responsible for identifying new trends relating to safety, fraud and abuse on GitHub, building agentic solutions to detect this abuse at scale, identifying vulnerabilities in GitHub that lead to abuse and helping to measure the impact of our work to safeguard the platform. At GitHub, Safety and Integrity's mission is to ensure GitHub and our users' safety through fighting malware, spam and fraud, monitoring for fake accounts, countering inauthentic content, battling crypto mining, and other core areas. You will be involved in collaborations across teams within GitHub including with Copilot and setting the standard for effective and responsible use of AI for moderation and trust and safety purposes, ensuring fraud is countered, content is moderated, users are kept safe and the open-source community can flourish.
If you have a strong foundation in large language models, solid software engineering instincts, a working knowledge of online platform trust and safety issues, and an empathetic approach to collaborating with a diverse team from entry-level associates to seasoned senior contributors, then this might be the gig for you.
WHAT WE VALUE
Collaboration: We believe the best work is done together.
Empathy: We believe in putting people first.
Quality: We believe in setting the standard for excellence.
Positive Impact: We believe in making the world a better place through our work.
* Shipping: We believe in creating things for the people using them.
Responsibilities
Design, build and deploy agentic solutions that leverage large language models to detect and prevent fraud, abuse, and security threats at scale — applying LLMs to problems such as content classification and multi-step agentic investigation.
Build well-engineered, production-grade systems that run reliably against high-volume event streams, making effective use of AI coding assistants to accelerate and improve your work.
Build and operate scalable ML systems on cloud platforms (such as Azure AI Foundry) for training, deploying, and serving models and agentic solutions in production.
Evaluate and improve existing models and agentic solutions using offline evaluations (including tool-use loops and LLM-as-judge evaluation), performance metrics, and feedback from operational deployments.
Identify vulnerabilities in products that lead to abuse, and provide consultation to product teams reviewing new features.
Collaborate closely with cross-functional teams including data scientists, software engineers, product managers and content moderators to integrate agentic solutions into production systems.
* Document the systems you help build and support the technical growth of your peers.
QUALIFICATIONS
Required Qualifications
* 4+ years experience in machine learning, or related field
+ OR Bachelor's Degree in Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field AND 2+ years experience in machine learning, or related field
+ OR Master's Degree in Machine Learning, Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field
+ OR equivalent experience.
Preferred Qualifications
Strong understanding of large language models — how they work — and hands-on experience applying them at scale, ideally for classification, agentic workflows, or agents.
Strong software engineering skills, including experience building with AI coding assistants.
Experience designing or evaluating agentic systems (tool-use loops, multi-step workflows, or LLM-as-judge evaluation).
Hands-on experience building and operating classification or detection systems at scale, including handling imbalanced data and precision/recall tradeoffs.
Experience in Trust and Safety, National Security or fighting spam, malware, fraud, and threat actor activity at scale.
Experience in responsible AI.
Experience in Safety-by-Design.
Experience with managing user data and privacy.
* Solid understanding of machine learning algorithms (supervised and unsupervised learning, anomaly detection, etc.) and their practical implementation.
COMPENSATION RANGE
The base salary range for this job is USD $107,700.00 - USD $285,900.00 /Yr.
These pay ranges are intended to cover roles based across the United States. An individual's base pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant. At GitHub certain roles are eligible for benefits and additional rewards, including annual bonus and stock. These rewards are allocated based on individual impact in role. In addition, certain roles also have the opportunity to earn sales incentives based on revenue or utilization, depending on the terms of the plan and the employee's role.
This position will be open for a minimum of 3 days, with applications accepted on an ongoing basis until the position is filled.
GITHUB LEADERSHIP PRINCIPLES
GitHub values
Customer-obsessed
Ship to learn
Growth mindset
Own the outcome
Better together
Diverse and inclusive
Manager fundamentals
Model
Coach
* Care
Leadership principles
Create clarity
Generate energy
* Deliver success
WHO WE ARE
GitHub is the world’s leading AI-powered developer platform with 150 million developers and counting. We’re also home to the biggest open-source community on earth (and 99% of the world’s software has open-source code in its DNA). Many of the apps and programs you use every day are built on GitHub.
Our teams are dreamers, doers, and pioneers, leading the way in AI, driving humanitarian efforts around the globe, and even sending open source to Mars (and beyond!). At GitHub, our goal is to create the space you need to do your best work. We’re remote-first and offer competitive pay, generous learning and growth opportunities, and excellent benefits to support you, wherever you are—because we know that people flourish when they can work on their own terms.
Join us, and let’s change the world, together.
EEO STATEMENT
GitHub is made up of people from a wide variety of backgrounds and lifestyles. We embrace diversity and invite applications from people of all walks of life. We don't discriminate against employees or applicants based on gender identity or expression, sexual orientation, race, religion, age, national origin, citizenship, disability, pregnancy status, veteran status, or any other differences. Also, if you have a disability, please let us know if there's any way we can make the interview process better for you; we're happy to accommodate!
ABOUT GITHUB
GitHub is the world’s leading platform for agentic software development — powered by Copilot to build, scale, and deliver secure software. Over 180 million developers, including more than 90% of the Fortune 100 companies, use GitHub to collaborate, and more than 77,000 organisations have adopted GitHub Copilot.
LOCATIONS
In this role you can work from Remote, United States
Overview
GitHub is changing the way the world builds software and we want you to help build and secure GitHub. We're looking for an experienced machine learning engineer to help design, build and deploy agentic solutions, and to conduct ad-hoc analysis, as you help protect the home of all developers.
You will be responsible for identifying new trends relating to safety, fraud and abuse on GitHub, building agentic solutions to detect this abuse at scale, identifying vulnerabilities in GitHub that lead to abuse and helping to measure the impact of our work to safeguard the platform. At GitHub, Safety and Integrity's mission is to ensure GitHub and our users' safety through fighting malware, spam and fraud, monitoring for fake accounts, countering inauthentic content, battling crypto mining, and other core areas. You will be involved in collaborations across teams within GitHub including with Copilot and setting the standard for effective and responsible use of AI for moderation and trust and safety purposes, ensuring fraud is countered, content is moderated, users are kept safe and the open-source community can flourish.
If you have a strong foundation in large language models, solid software engineering instincts, a working knowledge of online platform trust and safety issues, and an empathetic approach to collaborating with a diverse team from entry-level associates to seasoned senior contributors, then this might be the gig for you.
WHAT WE VALUE
Collaboration: We believe the best work is done together.
Empathy: We believe in putting people first.
Quality: We believe in setting the standard for excellence.
Positive Impact: We believe in making the world a better place through our work.
* Shipping: We believe in creating things for the people using them.
Responsibilities
Design, build and deploy agentic solutions that leverage large language models to detect and prevent fraud, abuse, and security threats at scale — applying LLMs to problems such as content classification and multi-step agentic investigation.
Build well-engineered, production-grade systems that run reliably against high-volume event streams, making effective use of AI coding assistants to accelerate and improve your work.
Build and operate scalable ML systems on cloud platforms (such as Azure AI Foundry) for training, deploying, and serving models and agentic solutions in production.
Evaluate and improve existing models and agentic solutions using offline evaluations (including tool-use loops and LLM-as-judge evaluation), performance metrics, and feedback from operational deployments.
Identify vulnerabilities in products that lead to abuse, and provide consultation to product teams reviewing new features.
Collaborate closely with cross-functional teams including data scientists, software engineers, product managers and content moderators to integrate agentic solutions into production systems.
* Document the systems you help build and support the technical growth of your peers.
QUALIFICATIONS
Required Qualifications
* 4+ years experience in machine learning, or related field
+ OR Bachelor's Degree in Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field AND 2+ years experience in machine learning, or related field
+ OR Master's Degree in Machine Learning, Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field
+ OR equivalent experience.
Preferred Qualifications
Strong understanding of large language models — how they work — and hands-on experience applying them at scale, ideally for classification, agentic workflows, or agents.
Strong software engineering skills, including experience building with AI coding assistants.
Experience designing or evaluating agentic systems (tool-use loops, multi-step workflows, or LLM-as-judge evaluation).
Hands-on experience building and operating classification or detection systems at scale, including handling imbalanced data and precision/recall tradeoffs.
Experience in Trust and Safety, National Security or fighting spam, malware, fraud, and threat actor activity at scale.
Experience in responsible AI.
Experience in Safety-by-Design.
Experience with managing user data and privacy.
* Solid understanding of machine learning algorithms (supervised and unsupervised learning, anomaly detection, etc.) and their practical implementation.
COMPENSATION RANGE
The base salary range for this job is USD $107,700.00 - USD $285,900.00 /Yr.
These pay ranges are intended to cover roles based across the United States. An individual's base pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant. At GitHub certain roles are eligible for benefits and additional rewards, including annual bonus and stock. These rewards are allocated based on individual impact in role. In addition, certain roles also have the opportunity to earn sales incentives based on revenue or utilization, depending on the terms of the plan and the employee's role.
This position will be open for a minimum of 3 days, with applications accepted on an ongoing basis until the position is filled.
GITHUB LEADERSHIP PRINCIPLES
GitHub values
Customer-obsessed
Ship to learn
Growth mindset
Own the outcome
Better together
Diverse and inclusive
Manager fundamentals
Model
Coach
* Care
Leadership principles
Create clarity
Generate energy
* Deliver success
WHO WE ARE
GitHub is the world’s leading AI-powered developer platform with 150 million developers and counting. We’re also home to the biggest open-source community on earth (and 99% of the world’s software has open-source code in its DNA). Many of the apps and programs you use every day are built on GitHub.
Our teams are dreamers, doers, and pioneers, leading the way in AI, driving humanitarian efforts around the globe, and even sending open source to Mars (and beyond!). At GitHub, our goal is to create the space you need to do your best work. We’re remote-first and offer competitive pay, generous learning and growth opportunities, and excellent benefits to support you, wherever you are—because we know that people flourish when they can work on their own terms.
Join us, and let’s change the world, together.
EEO STATEMENT
GitHub is made up of people from a wide variety of backgrounds and lifestyles. We embrace diversity and invite applications from people of all walks of life. We don't discriminate against employees or applicants based on gender identity or expression, sexual orientation, race, religion, age, national origin, citizenship, disability, pregnancy status, veteran status, or any other differences. Also, if you have a disability, please let us know if there's any way we can make the interview process better for you; we're happy to accommodate!
See All 496+ Remote Machine Learning Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote Machine Learning JobsRemote Machine Learning Job Market
Who's Hiring
- CVS Health34
- Alvarez & Marsal26
- Netflix20
- Liberty Mutual Insurance14
- Airbnb12
Top Industries Hiring
- Technology & Software145
- Consulting & Professional Services62
- Healthcare & Medical Services54
- Investment & Asset Management30
- Accounting & Auditing27
What Employers Look For
The qualifications that appear most often in remote machine learning jobs.
- Proficiency in Python and at least one major ML framework such as PyTorch or TensorFlow
- Experience designing, training, and deploying machine learning models in production environments
- Strong foundation in statistics, linear algebra, and probability theory
- Familiarity with cloud platforms such as AWS, GCP, or Azure for ML workloads
- Experience with data pipelines, feature engineering, and model evaluation workflows
- Bachelor's or master's degree in computer science, statistics, mathematics, or a related field
Tips for Your Remote Machine Learning Job Search
Build a portfolio that shows async work
Remote machine learning employers want evidence you can work without hand-holding. Document your projects end-to-end on GitHub with clear READMEs, reproducible notebooks, and model cards that explain your decisions. Async-readable documentation is itself a signal you'll thrive on a distributed team.
Demonstrate written communication from the start
Remote ML teams run on written communication, and your application materials are the first test. Write cover letters and follow-ups that are specific, structured, and easy to read quickly. Employers screening remote candidates treat clear written reasoning as a proxy for how you'll collaborate across time zones.
Target remote-first companies explicitly
Remote-first firms and AI-native startups build their processes around distributed teams, making them far more likely to hire and retain remote machine learning engineers long-term. Filter your search for companies with fully remote or distributed-by-design structures rather than legacy employers experimenting with hybrid arrangements.
Apply early to remote roles that fit
Migrate Mate lists remote machine learning openings from across the U.S. in one place, so you can find roles that match your skills and apply directly without sifting through location-filtered results. Applying in the first days a role is listed improves your odds before hiring pipelines fill.
Prepare for asynchronous remote interviews
Many remote machine learning interview processes include take-home assignments, recorded video responses, or async technical reviews before any live call. Practice explaining your model choices and tradeoffs in writing and in short recorded walkthroughs so you perform well in both synchronous and asynchronous formats.
Remote Machine Learning Jobs: Frequently Asked Questions
How do I get a remote machine learning job?
Target companies that already run distributed engineering teams, since they have the infrastructure and culture for remote machine learning work. Remote employers screen hard for self-direction, clear async written communication, and hands-on fluency with ML frameworks like PyTorch or TensorFlow. A public GitHub portfolio with documented experiments, model cards, and reproducible results gives you a concrete edge over candidates who rely on credentials alone.
Which companies hire remote machine learnings?
Employers currently hiring remote machine learnings include CVS Health, Alvarez & Marsal, and Netflix, per current remote listings on Migrate Mate as of June 2026. Remote-first technology companies, AI-native startups, and distributed teams in finance and healthcare tend to hire for this role across the U.S. without location requirements.
Can you get a remote machine learning job with no experience?
Yes, but remote entry-level machine learning roles are harder to land because employers expect you to work independently from day one without in-person mentorship. AI-native startups and research-focused companies are the most open to junior candidates. A strong public portfolio of end-to-end ML projects, contributions to open-source repositories, and demonstrated async communication skills can open doors that a thin resume alone cannot.
Do you need a degree for remote machine learning jobs?
Not always. Many remote employers weigh demonstrable skills and project results heavily alongside or instead of a formal degree, particularly at startups and AI-product companies. What matters most is evidence you can build, train, evaluate, and deploy models independently. A portfolio showing real ML pipelines, published Kaggle results, or open-source contributions often carries more weight than the credential alone.
Which industries hire the most remote machine learnings?
Remote machine learning roles concentrate in Technology & Software, Consulting & Professional Services, and Healthcare & Medical Services, based on current remote listings on Migrate Mate as of June 2026. Those sectors rely on distributed engineering teams that can build and iterate on models without requiring engineers to be in a central office.
See All 496+ Remote Machine Learning Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote Machine Learning Jobs