Remote ML Software Engineer Jobs
Remote ML Software Engineer jobs are open across the U.S. in sectors like tech, fintech, healthcare AI, and enterprise software, at remote-first companies and distributed engineering teams ranging from early-stage startups to large-scale platform businesses. Employers hiring remotely right now include CVS Health, Alvarez & Marsal, and Netflix. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 496+ Remote ML Software Engineer 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!
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Find JobsRemote ML Software Engineer 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 ML software engineer 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
- Familiarity with MLOps practices including experiment tracking, model versioning, and CI/CD pipelines
- Strong foundations in statistics, probability, and linear algebra relevant to model development
- Bachelor's or master's degree in computer science, electrical engineering, or a related quantitative field
- Experience with cloud platforms such as AWS, Google Cloud, or Azure for scalable model serving
Tips for Your Remote ML Software Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote ml software engineer openings from companies across the U.S. in one place. Filter by role and apply directly to the ones that match your stack and experience level before competitive postings fill.
Build a portfolio that proves remote output
Remote hiring managers can't see you work, so your GitHub, Hugging Face repos, or personal project writeups do that for you. Document your decisions, not just your code, so reviewers understand your reasoning process without a conversation.
Demonstrate async communication in your application
Remote ml software engineer teams run on written communication. A cover message or technical summary that is clear, structured, and self-contained signals you'll function well in a distributed team, often more than a polished resume alone.
Match your tools to the remote ML stack
Remote ML teams commonly rely on MLflow, Weights and Biases, Kubeflow, or similar experiment tracking and orchestration tools alongside cloud platforms like AWS SageMaker or Google Vertex AI. Calling these out specifically in your application materials shows operational readiness, not just modeling knowledge.
Prepare for asynchronous remote interview rounds
Many remote-first companies include a take-home ML system design or a recorded technical screen early in their process. Practice articulating model tradeoffs and architecture decisions in writing, not just verbally, since that mirrors how remote teams actually evaluate and ship ML work.
Remote ML Software Engineer Jobs: Frequently Asked Questions
How do I get a remote ml software engineer job?
Target remote-first companies and distributed engineering teams, which make up the bulk of remote ml software engineer openings. Remote employers screen heavily for async communication skills, self-directed project execution, and hands-on fluency with ML frameworks, experiment tracking tools, and model deployment pipelines. A public portfolio of shipped ML work, clean documentation habits, and a history of contributing to collaborative codebases gives you a clear edge over candidates who have only worked in-office environments.
Which companies hire remote ml software engineers?
Remote ml software engineer roles are posted by CVS Health, Alvarez & Marsal, and Netflix and others right now, based on current remote listings on Migrate Mate as of June 2026. These tend to be remote-first tech firms, distributed AI product teams, and companies in fintech, healthcare technology, and enterprise SaaS that run fully asynchronous engineering organizations.
Can you get a remote ml software engineer job with no experience?
Yes, but remote entry-level ml software engineer roles are harder to land than in-office ones because you're expected to work independently from day one. Companies that hire entry-level ml software engineers remotely are usually early-stage startups or open-source-driven teams. A strong GitHub portfolio with documented ML projects, contributions to public repositories, and demonstrated ability to communicate technical decisions in writing can substitute for formal work history.
Do you need a degree for remote ml software engineer jobs?
Not always. Remote employers in ML consistently weigh demonstrable technical skills, shipped projects, and measurable results over formal credentials. A portfolio showing end-to-end ML work, from data preprocessing through model evaluation and deployment, carries significant weight. That said, roles at larger companies or those involving research-adjacent work still frequently list a bachelor's or master's in computer science, mathematics, or a related field as a baseline requirement.
Which industries hire the most remote ml software engineers?
Remote ml software engineer roles concentrate in Technology & Software, Consulting & Professional Services, and Healthcare & Medical Services, based on current remote listings on Migrate Mate as of June 2026. These sectors rely on distributed ML teams because their core products, whether AI-driven software platforms, algorithmic financial systems, or clinical decision tools, are built and iterated entirely in code that requires no physical presence.
See All 496+ Remote ML Software Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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