Machine Learning Engineer Jobs
Machine Learning Engineer jobs are open across technology, finance, healthcare, and autonomous systems, from new-grad to staff and principal level, with specializations in natural language processing, computer vision, and recommendation systems. Find a role that fits from the openings below and apply directly.
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Showing 5 of 2,140+ Machine Learning Engineer jobs
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 Engineer Jobs by Experience Level
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Find JobsMachine Learning Engineer Job Market
Who's Hiring
- TikTok195
- Apple158
- ByteDance71
- Meta64
- Google58
Top Industries Hiring
- Technology & Software146
- Electronics & Hardware56
- Banking & Financial Services28
- Automotive26
- Biotechnology & Pharmaceuticals13
What Employers Look For
The qualifications that appear most often in 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 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.
Machine Learning Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most machine learning engineers?
The companies hiring the most machine learning engineers right now include TikTok, Apple, and ByteDance, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of September 2026. Demand is concentrated in companies scaling generative AI, recommendation systems, and computer vision products.
How many machine learning engineer jobs are remote?
About 58% of machine learning engineer openings are fully remote or hybrid as of September 2026, making it one of the more flexible roles in software. Positions focused on NLP research and MLOps tooling tend to offer the highest share of remote flexibility, while roles tied to robotics or on-premise infrastructure typically require in-person work.
How do you become a machine learning engineer?
Start by building a strong foundation in Python, linear algebra, and statistics, then work through core ML concepts using publicly available datasets and open-source frameworks like PyTorch or scikit-learn. Add hands-on projects that go beyond notebooks to include model deployment and monitoring. A portfolio showing end-to-end ML systems carries more weight in hiring than coursework alone.
Can you get a machine learning engineer job with little or no experience?
Yes, entry-level machine learning engineer roles exist, particularly at startups and in companies building internal ML tooling. Focus your portfolio on projects that solve a real problem and deploy to a live endpoint. Contributing to open-source ML libraries, competing in public benchmarks, and demonstrating strong software engineering fundamentals will distinguish you from other early-career candidates.
What does the machine learning engineer interview process look like?
Most machine learning engineer loops include a recruiter screen, a technical phone interview covering Python and ML fundamentals, a take-home or live coding exercise, an ML system design session, and a final round with cross-functional team members. System design interviews often focus on topics like feature pipelines, model serving, and retraining strategies rather than abstract algorithms.
Where can I find and apply to machine learning engineer jobs?
You can find and apply to machine learning engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and specialization, then apply directly to each listing. The openings on this page are updated regularly so you can act on new postings as they appear.
See All 2,140+ Machine Learning Engineer Jobs
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