Remote Machine Learning Engineer Jobs
Remote Machine Learning Engineer jobs are open across the US at companies hiring remotely, from entry-level roles at remote-first startups to senior roles on large distributed teams, with employers like CVS Health, Alvarez & Marsal, and Netflix hiring right now. See the openings below and apply to the ones that match your experience.
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Showing 5 of 496+ Remote Machine Learning 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!
See All 496+ Remote Machine Learning Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find JobsRemote Machine Learning 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 machine learning engineer jobs.
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience building and deploying models in cloud environments like AWS, GCP, or Azure
- Familiarity with MLOps tools including MLflow, Kubeflow, or SageMaker
- Strong foundation in statistics, linear algebra, and machine learning theory
- Bachelor's or master's degree in computer science, mathematics, or a related field
- Experience with large-scale data processing using Spark, SQL, or distributed systems
Tips for Your Remote Machine Learning Engineer Job Search
Tailor your resume to deployment depth
Hiring managers distinguish candidates who trained models from those who shipped them to production. Explicitly note the serving infrastructure you used, the scale you operated at, and whether you owned monitoring and retraining pipelines, not just model accuracy metrics.
Build a GitHub portfolio that shows end-to-end work
Recruiters and engineers scan repositories for evidence you can move from raw data to a deployed artifact. Include notebooks, a training script, an inference endpoint, and a brief README explaining the problem you solved and what tradeoffs you made.
Apply early to roles that fit
Migrate Mate lists machine learning engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by the ML stack you know best
Job postings for machine learning engineers vary sharply by framework, cloud platform, and data scale. Prioritize listings that name PyTorch, TensorFlow, JAX, or the specific cloud ML services you have hands-on experience with rather than applying broadly.
Prepare for system design questions alongside coding rounds
Most machine learning engineer interview loops include at least one session on designing scalable ML systems, such as a real-time feature store or an online ranking pipeline. Practice articulating latency budgets, retraining frequency, and data consistency tradeoffs out loud before your first screen.
Negotiate around compute budgets and research time
Beyond base compensation, ask about GPU or TPU access, experiment tracking tooling, and whether engineers are allocated time for internal research. These factors affect your ability to do meaningful work and are often negotiable, especially at mid-size companies.
Remote Machine Learning Engineer Jobs: Frequently Asked Questions
How do I get a remote machine learning engineer job?
Target companies that already run distributed teams, since they hire remotely by default and know how to onboard someone they never meet in person. Remote machine learning engineer employers screen hard for self-direction and clear written communication on top of the core skills, so show evidence you can own work without someone over your shoulder. Apply to the openings above that match your experience.
Which companies hire remote machine learning engineers?
Companies hiring remote machine learning engineers include CVS Health, Alvarez & Marsal, and Netflix, based on current remote listings on Migrate Mate as of June 2026. Remote-first firms and large companies running distributed teams post the most remote machine learning engineer roles.
Can you get a remote machine learning engineer job with no experience?
Yes, but it is harder than an on-site role, because remote work expects you to operate independently from the start. Entry-level remote machine learning engineer openings do exist, especially at remote-first companies, and a portfolio of real work helps more than a long resume. Applying broadly to the roles that fit improves your odds.
Do you need a degree for remote machine learning engineer jobs?
Not always. Many employers hire remote machine learning engineers on demonstrated skills and prior work rather than a specific degree, though some larger companies still prefer one. Showing relevant results matters more than a credential for most remote machine learning engineer roles.
Which industries hire the most remote machine learning engineers?
The sectors hiring the most remote machine learning engineers are Technology & Software, Consulting & Professional Services, and Healthcare & Medical Services, based on current remote listings on Migrate Mate as of June 2026. These sectors run distributed teams and hire machine learning engineers remotely most consistently.
See All 496+ Remote Machine Learning Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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