Machine Learning Intern Jobs
Machine Learning Intern jobs are open across tech, healthcare, finance, and research institutions, at levels from undergraduate through graduate, with specializations in natural language processing, computer vision, and predictive modeling. Scan the live roles below and apply to whichever ones fit.
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Job Description:
Integral Ad Science (IAS) is a global technology and data company that builds verification, optimization, and analytics solutions for the advertising industry, and we’re looking for a Staff Machine Learning Engineer on the Data Science Team. If you are excited by technology that has the power to handle hundreds of thousands of transactions per second; collect tens of billions of events each day; and evaluate thousands of data points in real-time all while responding in just a few milliseconds, then IAS is the place for you!
As a Staff Machine Learning Engineer at IAS, you will be part of a team that is at the center of innovation for the company and a major contributor to our core products. You will oversee a sophisticated suite of data science systems making large-scale business predictions across the open web, social networks, video, and mobile apps. At the Staff level, you are expected to be a technical pillar for the organization. You will take ownership of open, highly ambiguous business problems and translate them into scalable ML architectures. You will define technical roadmaps, set the standard for ML engineering practices, and push the boundaries of applied machine learning to deliver best-in-class solutions for our clients. Innovation is at the heart of our competitive advantage, and you will cultivate it by mentoring talent and raising the technical bar across multiple teams. The types of challenges we solve have attracted people from industry and academia with diverse backgrounds. We’re passionate about maintaining an open and collaborative environment, where team members bring their own unique style of thinking and tools to the table.
What you’ll get to do:
- Technical Leadership & Vision: Drive the architectural vision and system design for our core AI/ML-based services.
- Act as the technical lead for complex, multi-quarter initiatives from inception to global deployment.
- Architect at Scale: Design and build large-scale deep learning infrastructure and platforms for distributed model training, ensuring low-latency and high-availability at enterprise scale.
- Cross-Functional Influence: Partner with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy.
- Translate abstract product requirements into concrete technical designs.
- Act as a Multiplier: Mentor and guide senior and mid-level data scientists and engineers.
- Establish standard methodologies, define code quality expectations, and lead architecture design reviews.
- Infrastructure Mastery: Work with large-scale AI training infra components (accelerators, network fabrics, CUDA, NCCL, RDMA) and big data ecosystems (Databricks, Spark, Kubernetes, Kafka, Prometheus).
- End-to-End Ownership: Design, develop, and support robust CI/CD pipelines for AI/ML services, ensuring models transition smoothly from research into reliable production endpoints.
You should apply if you have most of this experience:
- PhD/Master’s degree in a technical field such as computer science, mathematics, statistics, or equivalent years of experience.
- 7+ years of machine learning experience in industry, with a proven track record of operating at a Lead, Principal, or Staff level.
- System Design & Architecture: Deep expertise in designing complex machine learning systems, ranking infrastructures, and data pipelines that handle massive throughput.
- ML Frameworks: Extensive, production-hardened experience working with frameworks such as PyTorch, JAX, or TensorFlow.
- Production Coding: Strong, production-grade programming skills in Python, Go, Java, or C++, along with a deep understanding of software design principles and algorithms.
- Ambiguity & Execution: Proven ability to thrive in ambiguity, take large unstructured problems, and independently drive them to successful technical resolutions.
- Mentorship: A strong history of collaborating with, elevating, and mentoring engineering talent.
IAS Pay Transparency:
The annualized base salary ranges for the primary location, and any additional locations are listed below. Our pay ranges are based on the work location. As part of IAS compensation package, we offer a comprehensive benefits package that includes paid time off, health insurance (medical, dental, vision) as well as PPO, HSA and FSA options and 401k with employer matching contributions. All full-time employee roles include competitive compensation and are eligible for an annual bonus and/or other incentive plans. Each candidate’s compensation package is based on multiple factors, but not limited to, geography, experience, skills, job duties, and business need.
Primary Location:
US - New York, NY
Primary Location Base Pay Range:
$135,100.00 - $231,600.00 Annual
Additional Locations:
USA - Remote
Additional Locations Pay Range:
$119,000.00 - $204,000.00 USD Annual
About Integral Ad Science:
Integral Ad Science (IAS) is a leading global media measurement and optimization platform that delivers the industry’s most actionable data to drive superior results for the world’s largest advertisers, publishers, and media platforms. IAS’s software provides comprehensive and enriched data that ensures ads are seen by real people in safe and suitable environments, while improving return on ad spend for advertisers and yield for publishers. Our mission is to be the global benchmark for trust and transparency in digital media quality. For more information, visit integralads.com.
Equal Opportunity Employer:
IAS is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities and veterans to apply.
California Applicant Pre-Collection Notice:
We collect personal information (PI) from you in connection with your application for employment or engagement with IAS, including the following categories of PI: identifiers, personal records, commercial information, professional or employment or engagement information, non-public education records, and inferences drawn from your PI. We collect your PI for our purposes, including performing services and operations related to your potential employment or engagement. For additional details or if you have questions, contact us at compliance@integralads.com.
Attention agency/3rd party recruiters: IAS does not accept any unsolicited resumes or candidate profiles. If you are interested in becoming an IAS recruiting partner, please send an email introducing your company to recruitingagencies@integralads.com. We will get back to you if there's interest in a partnership.
Job Description:
Integral Ad Science (IAS) is a global technology and data company that builds verification, optimization, and analytics solutions for the advertising industry, and we’re looking for a Staff Machine Learning Engineer on the Data Science Team. If you are excited by technology that has the power to handle hundreds of thousands of transactions per second; collect tens of billions of events each day; and evaluate thousands of data points in real-time all while responding in just a few milliseconds, then IAS is the place for you!
As a Staff Machine Learning Engineer at IAS, you will be part of a team that is at the center of innovation for the company and a major contributor to our core products. You will oversee a sophisticated suite of data science systems making large-scale business predictions across the open web, social networks, video, and mobile apps. At the Staff level, you are expected to be a technical pillar for the organization. You will take ownership of open, highly ambiguous business problems and translate them into scalable ML architectures. You will define technical roadmaps, set the standard for ML engineering practices, and push the boundaries of applied machine learning to deliver best-in-class solutions for our clients. Innovation is at the heart of our competitive advantage, and you will cultivate it by mentoring talent and raising the technical bar across multiple teams. The types of challenges we solve have attracted people from industry and academia with diverse backgrounds. We’re passionate about maintaining an open and collaborative environment, where team members bring their own unique style of thinking and tools to the table.
What you’ll get to do:
- Technical Leadership & Vision: Drive the architectural vision and system design for our core AI/ML-based services.
- Act as the technical lead for complex, multi-quarter initiatives from inception to global deployment.
- Architect at Scale: Design and build large-scale deep learning infrastructure and platforms for distributed model training, ensuring low-latency and high-availability at enterprise scale.
- Cross-Functional Influence: Partner with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy.
- Translate abstract product requirements into concrete technical designs.
- Act as a Multiplier: Mentor and guide senior and mid-level data scientists and engineers.
- Establish standard methodologies, define code quality expectations, and lead architecture design reviews.
- Infrastructure Mastery: Work with large-scale AI training infra components (accelerators, network fabrics, CUDA, NCCL, RDMA) and big data ecosystems (Databricks, Spark, Kubernetes, Kafka, Prometheus).
- End-to-End Ownership: Design, develop, and support robust CI/CD pipelines for AI/ML services, ensuring models transition smoothly from research into reliable production endpoints.
You should apply if you have most of this experience:
- PhD/Master’s degree in a technical field such as computer science, mathematics, statistics, or equivalent years of experience.
- 7+ years of machine learning experience in industry, with a proven track record of operating at a Lead, Principal, or Staff level.
- System Design & Architecture: Deep expertise in designing complex machine learning systems, ranking infrastructures, and data pipelines that handle massive throughput.
- ML Frameworks: Extensive, production-hardened experience working with frameworks such as PyTorch, JAX, or TensorFlow.
- Production Coding: Strong, production-grade programming skills in Python, Go, Java, or C++, along with a deep understanding of software design principles and algorithms.
- Ambiguity & Execution: Proven ability to thrive in ambiguity, take large unstructured problems, and independently drive them to successful technical resolutions.
- Mentorship: A strong history of collaborating with, elevating, and mentoring engineering talent.
IAS Pay Transparency:
The annualized base salary ranges for the primary location, and any additional locations are listed below. Our pay ranges are based on the work location. As part of IAS compensation package, we offer a comprehensive benefits package that includes paid time off, health insurance (medical, dental, vision) as well as PPO, HSA and FSA options and 401k with employer matching contributions. All full-time employee roles include competitive compensation and are eligible for an annual bonus and/or other incentive plans. Each candidate’s compensation package is based on multiple factors, but not limited to, geography, experience, skills, job duties, and business need.
Primary Location:
US - New York, NY
Primary Location Base Pay Range:
$135,100.00 - $231,600.00 Annual
Additional Locations:
USA - Remote
Additional Locations Pay Range:
$119,000.00 - $204,000.00 USD Annual
About Integral Ad Science:
Integral Ad Science (IAS) is a leading global media measurement and optimization platform that delivers the industry’s most actionable data to drive superior results for the world’s largest advertisers, publishers, and media platforms. IAS’s software provides comprehensive and enriched data that ensures ads are seen by real people in safe and suitable environments, while improving return on ad spend for advertisers and yield for publishers. Our mission is to be the global benchmark for trust and transparency in digital media quality. For more information, visit integralads.com.
Equal Opportunity Employer:
IAS is an equal opportunity employer, committed to our diversity and inclusiveness. We will consider all qualified applicants without regard to race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities and veterans to apply.
California Applicant Pre-Collection Notice:
We collect personal information (PI) from you in connection with your application for employment or engagement with IAS, including the following categories of PI: identifiers, personal records, commercial information, professional or employment or engagement information, non-public education records, and inferences drawn from your PI. We collect your PI for our purposes, including performing services and operations related to your potential employment or engagement. For additional details or if you have questions, contact us at compliance@integralads.com.
Attention agency/3rd party recruiters: IAS does not accept any unsolicited resumes or candidate profiles. If you are interested in becoming an IAS recruiting partner, please send an email introducing your company to recruitingagencies@integralads.com. We will get back to you if there's interest in a partnership.
Machine Learning Intern Jobs by Experience Level
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Who's Hiring
- TikTok184
- Apple129
- ByteDance53
- Meta48
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Top Industries Hiring
- Technology & Software134
- Electronics & Hardware33
- Biotechnology & Pharmaceuticals22
- Science & Research14
- Banking & Financial Services14
What Employers Look For
The qualifications that appear most often in machine learning intern jobs.
- Proficiency in Python and at least one ML framework such as PyTorch or TensorFlow
- Hands-on experience with data preprocessing, feature engineering, and model evaluation
- Familiarity with machine learning concepts including supervised, unsupervised, and deep learning
- Currently enrolled in or recently completed a bachelor's or master's degree in computer science, data science, or a related field
- Experience with data manipulation libraries such as NumPy, pandas, or scikit-learn
- Demonstrated project work or research published in a portfolio, GitHub repository, or academic paper
Tips for Your Machine Learning Intern Job Search
Lead your resume with project impact
Employers scan for measurable outcomes, not just tools used. Quantify what your models actually achieved, such as accuracy improvements or inference speed gains, rather than listing frameworks. Descriptions like 'improved classification accuracy by reducing false positives on a held-out test set' outperform generic skill lists.
Post your notebooks before you apply
Most machine learning intern reviewers look for a GitHub or portfolio link before reading your resume. Push clean, commented notebooks for your two or three strongest projects, add a brief README explaining the problem and your approach, and include that link prominently on your resume and application.
Apply early to roles that fit
Migrate Mate lists machine learning intern openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Match your keywords to each job description
Hiring teams often filter by specific frameworks or research areas. If a posting calls out PyTorch, reinforcement learning, or time-series forecasting, mirror that exact language in your resume where it honestly applies. Generic ML resumes get filtered out before a human reviewer sees them.
Prepare to explain your training pipeline end to end
Technical screens for machine learning interns almost always include a walkthrough of a past project. Practice explaining your data preprocessing choices, model selection rationale, and evaluation metrics out loud. Interviewers probe where you made trade-offs, not just whether your model worked.
Follow up with a specific technical observation
After a technical interview, send a follow-up note that references one concrete detail from the conversation, such as an alternative architecture you mentioned or a dataset edge case you discussed. It signals genuine engagement and keeps your name tied to the technical substance of the interview.
Machine Learning Intern Jobs: Frequently Asked Questions
Which companies are hiring the most machine learning interns?
The most active employers for machine learning interns right now are TikTok, Apple, and ByteDance, and the most openings are in California, Washington, and New York, based on current listings on Migrate Mate as of September 2026. Demand is concentrated at technology companies, financial services firms, and research-oriented healthcare organizations.
How many machine learning intern jobs are remote?
About 55% of machine learning intern openings are fully remote or hybrid as of September 2026, reflecting how much of the work centers on code and experimentation rather than on-site hardware. Roles focused on NLP research and software-side model development tend to be the most remote-friendly, while positions involving specialized compute infrastructure or lab data are more likely to require on-site presence.
How do you become a machine learning intern?
Start by building a foundation in Python, linear algebra, probability, and core ML algorithms through coursework or self-study. Then complete two or three end-to-end projects, from data collection through model evaluation, and publish them in a public repository. Apply to roles that match your current skill set, and tailor each application to the specific research area or product focus listed in the posting.
Can you get a machine learning intern role with little experience?
Yes, focused project work can substitute for professional experience at the intern level. Build and document a complete ML project, even on a public dataset, that walks through problem framing, preprocessing, modeling, and evaluation. Courses with hands-on assignments, Kaggle competition write-ups, and open-source contributions all signal practical readiness to reviewers who know you haven't held a prior ML role.
What does the machine learning intern interview process look like?
The process typically starts with a recruiter screen, followed by a technical phone interview covering ML fundamentals, probability, and coding. A take-home or live coding challenge often follows, asking you to train or debug a model on a small dataset. Final rounds usually include a project deep-dive where you walk through your own work, and a conversation with the team about research direction or product goals.
Where can I find and apply to machine learning intern jobs?
You can find and apply to machine learning intern jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your skills and research interests, then apply directly to each one that fits.
See All 1,717+ Machine Learning Intern Jobs
Find roles that match your experience and apply in just a few clicks.
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