{"schemaVersion":"jobsearcher.job.v1","id":"a2bcdd45a52ba1f63bb3cf93","url":"https://jobsearcher.com/jobs/a2bcdd45a52ba1f63bb3cf93","canonicalUrl":"https://jobsearcher.com/jobs/a2bcdd45a52ba1f63bb3cf93","title":"ML / AI Engineer - Remote","description":"# Machine Learning/AI Infrastructure Engineering Intern (AI Platform) — PhD, Winter 2027## About the RoleNetflix is seeking a **Machine Learning/AI Infrastructure Engineering Intern** to join the **AI Platform** team for Winter 2027\\. The AI Platform team builds the infrastructure that Netflix's ML and AI systems run on, including large-scale training platforms, post-training and offline infrastructure, and GPU-optimized inference and serving.This internship is designed for **PhD researchers** who enjoy working at the intersection of **machine learning and systems** rather than focusing exclusively on modeling. Interns will work on infrastructure closely co-designed with modeling teams through **model-system codesign**, solving open-ended infrastructure challenges at Netflix scale.## Key Responsibilities- Work on infrastructure supporting Netflix's machine learning and AI systems.- Contribute to large-scale ML training platforms and distributed training infrastructure.- Work on post-training and offline ML infrastructure.- Contribute to inference and serving optimization, including GPU-optimized inference.- Work at the intersection of machine learning and systems through model-system codesign.- Collaborate closely with modeling teams on ML and AI infrastructure.- Solve open-ended infrastructure challenges at Netflix scale.- Contribute within an AI Platform team based on interests and skill sets.## Required Qualifications- Currently enrolled in a **PhD program** in:- Computer Science- Distributed Systems- Systems- Networking- Machine Learning- Computer Engineering- A related field- Research or applied experience in **at least one** of the following:- Distributed systems- Distributed training or serving infrastructure- ML training platforms- Post-training or offline infrastructure- Inference and serving optimization- GPU-optimized inference- Model-system codesign- Proficiency in **Python**.- Familiarity with distributed compute frameworks such as **Ray, Kubernetes, or Spark** and ML training/serving stacks.- Curious, self-motivated approach to open-ended infrastructure challenges.- Strong written and verbal communication skills.- Must be a student returning to school for at least one semester/quarter after the internship.## Preferred Qualifications- Experience with systems programming languages such as **Go, C++, or Rust**.- Publications or strong research alignment with systems-track venues such as **OSDI, SOSP, or NSDI**.- Publications or research alignment with applied ML venues.- Prior industry experience in ML infrastructure.- Prior internship experience in ML infrastructure.## Skills & Competencies- Machine learning infrastructure- AI infrastructure- Distributed systems- Distributed training- Distributed serving- ML training platforms- Post-training infrastructure- Offline infrastructure- Inference optimization- GPU-optimized inference- ML serving- Model-system codesign- Python- Ray- Kubernetes- Spark- ML training and serving stacks- Systems programming- Problem-solving- Self-motivation- Written and verbal communication## Education & Experience**Education**- Currently pursuing a **PhD** in Computer Science, Distributed Systems, Systems, Networking, Machine Learning, Computer Engineering, or a related field.**Experience**- Research or applied experience in one or more relevant ML infrastructure, distributed systems, training, serving, inference, GPU optimization, or model-system codesign areas.- No specific years of experience are required.- Prior industry or ML infrastructure internship experience is preferred, not required.## Work Arrangement & Schedule- **Work Arrangement:** Remote candidates considered; the role is based at Netflix's **Los Gatos, CA headquarters**, with location flexibility for this team.- **Employment Type:** Internship- **Duration:** Minimum 12 weeks- **Start Date:** Second week of January 2027 / early January 2027- **Program:** Winter 2027- **Eligibility:** Intended for students returning to school for at least one semester/quarter after the internship.- **Application Timing:** Applications are reviewed on a rolling basis and remain open until roles are filled. The job is open for no less than 7 days and will be removed when the position is filled.## Compensation & Benefits- **Compensation:** Netflix internships typically have an overall market range of **$40/hour–$85/hour**, with compensation varying based on location, skills, experience, job, and other compensation factors.- **Benefits:** Internships are paid and include the benefits applicable to the internship program.## Compliance / Additional Information- Applicants must complete an **Airtable form** sent shortly after submitting the application on Netflix's careers site for the application to be considered complete.- Applicants should include a **Resume or CV** with complete contact information, relevant coursework, and publications if applicable.- Applicants will be asked to provide a short statement describing their research experiences and interests and, optionally, their relevance to Netflix Research.- Conversion or return offers are based on business need and headcount and are **not guaranteed**.- Netflix is an equal-opportunity employer and does not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.- Accommodation/adjustment requests for disabilities or other reasons during the hiring process can be made through the recruiting partner.- Netflix provides comprehensive benefits and support programs, including health plans, mental health support, 401(k) retirement plan with employer match, stock option program, disability programs, health savings and flexible spending accounts, family-forming benefits, life and serious injury benefits, and paid leave programs.","company":"Torentify","rawCompany":"torentify","city":"Denver","state":"CO","isRemote":true,"isActive":false,"createdAt":"2026-09-04T08:24:03.781Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"ML / AI Engineer - Remote","description":"# Machine Learning/AI Infrastructure Engineering Intern (AI Platform) — PhD, Winter 2027## About the RoleNetflix is seeking a **Machine Learning/AI Infrastructure Engineering Intern** to join the **AI Platform** team for Winter 2027\\. The AI Platform team builds the infrastructure that Netflix's ML and AI systems run on, including large-scale training platforms, post-training and offline infrastructure, and GPU-optimized inference and serving.This internship is designed for **PhD researchers** who enjoy working at the intersection of **machine learning and systems** rather than focusing exclusively on modeling. Interns will work on infrastructure closely co-designed with modeling teams through **model-system codesign**, solving open-ended infrastructure challenges at Netflix scale.## Key Responsibilities- Work on infrastructure supporting Netflix's machine learning and AI systems.- Contribute to large-scale ML training platforms and distributed training infrastructure.- Work on post-training and offline ML infrastructure.- Contribute to inference and serving optimization, including GPU-optimized inference.- Work at the intersection of machine learning and systems through model-system codesign.- Collaborate closely with modeling teams on ML and AI infrastructure.- Solve open-ended infrastructure challenges at Netflix scale.- Contribute within an AI Platform team based on interests and skill sets.## Required Qualifications- Currently enrolled in a **PhD program** in:- Computer Science- Distributed Systems- Systems- Networking- Machine Learning- Computer Engineering- A related field- Research or applied experience in **at least one** of the following:- Distributed systems- Distributed training or serving infrastructure- ML training platforms- Post-training or offline infrastructure- Inference and serving optimization- GPU-optimized inference- Model-system codesign- Proficiency in **Python**.- Familiarity with distributed compute frameworks such as **Ray, Kubernetes, or Spark** and ML training/serving stacks.- Curious, self-motivated approach to open-ended infrastructure challenges.- Strong written and verbal communication skills.- Must be a student returning to school for at least one semester/quarter after the internship.## Preferred Qualifications- Experience with systems programming languages such as **Go, C++, or Rust**.- Publications or strong research alignment with systems-track venues such as **OSDI, SOSP, or NSDI**.- Publications or research alignment with applied ML venues.- Prior industry experience in ML infrastructure.- Prior internship experience in ML infrastructure.## Skills & Competencies- Machine learning infrastructure- AI infrastructure- Distributed systems- Distributed training- Distributed serving- ML training platforms- Post-training infrastructure- Offline infrastructure- Inference optimization- GPU-optimized inference- ML serving- Model-system codesign- Python- Ray- Kubernetes- Spark- ML training and serving stacks- Systems programming- Problem-solving- Self-motivation- Written and verbal communication## Education & Experience**Education**- Currently pursuing a **PhD** in Computer Science, Distributed Systems, Systems, Networking, Machine Learning, Computer Engineering, or a related field.**Experience**- Research or applied experience in one or more relevant ML infrastructure, distributed systems, training, serving, inference, GPU optimization, or model-system codesign areas.- No specific years of experience are required.- Prior industry or ML infrastructure internship experience is preferred, not required.## Work Arrangement & Schedule- **Work Arrangement:** Remote candidates considered; the role is based at Netflix's **Los Gatos, CA headquarters**, with location flexibility for this team.- **Employment Type:** Internship- **Duration:** Minimum 12 weeks- **Start Date:** Second week of January 2027 / early January 2027- **Program:** Winter 2027- **Eligibility:** Intended for students returning to school for at least one semester/quarter after the internship.- **Application Timing:** Applications are reviewed on a rolling basis and remain open until roles are filled. The job is open for no less than 7 days and will be removed when the position is filled.## Compensation & Benefits- **Compensation:** Netflix internships typically have an overall market range of **$40/hour–$85/hour**, with compensation varying based on location, skills, experience, job, and other compensation factors.- **Benefits:** Internships are paid and include the benefits applicable to the internship program.## Compliance / Additional Information- Applicants must complete an **Airtable form** sent shortly after submitting the application on Netflix's careers site for the application to be considered complete.- Applicants should include a **Resume or CV** with complete contact information, relevant coursework, and publications if applicable.- Applicants will be asked to provide a short statement describing their research experiences and interests and, optionally, their relevance to Netflix Research.- Conversion or return offers are based on business need and headcount and are **not guaranteed**.- Netflix is an equal-opportunity employer and does not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.- Accommodation/adjustment requests for disabilities or other reasons during the hiring process can be made through the recruiting partner.- Netflix provides comprehensive benefits and support programs, including health plans, mental health support, 401(k) retirement plan with employer match, stock option program, disability programs, health savings and flexible spending accounts, family-forming benefits, life and serious injury benefits, and paid leave programs.","datePosted":"2026-09-04T08:24:03.781Z","dateModified":"2026-09-04T08:24:03.781Z","hiringOrganization":{"@type":"Organization","name":"Torentify","sameAs":"https://jobsearcher.com"},"jobLocationType":"TELECOMMUTE","applicantLocationRequirements":{"@type":"Country","name":"US"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"a2bcdd45a52ba1f63bb3cf93"},"url":"https://jobsearcher.com/jobs/a2bcdd45a52ba1f63bb3cf93"}}