Machine Learning Engineer Jobs at Netflix with Visa Sponsorship
Netflix builds its machine learning infrastructure at scale, and that ambition carries through to how it hires for ML engineering roles. The company sponsors a range of visa types for technical talent, making it a realistic target if you're an international candidate with strong model development or ML systems experience.
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At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.
The Team
The Studio Media Algorithms team is at the forefront of algorithmic innovation to enhance and support the creation of Netflix’s entertainment content, including games. In this role, you will be embedded within this team while collaborating very closely with a specialized Games Studio R&D team. This incubation-style team is chartered to lead our investments in building new kinds of games leveraging emerging technologies to support our creators and reach player audiences in new ways.
The Role
We are looking for a Machine Learning Engineer with a focus on MLOps, deployment, and performance optimization to help bridge the gap between research and production in the gaming space. You will work cross-functionally with games technical directors, designers, and scientists to ensure that novel AI-driven game concepts can be deployed efficiently across a variety of hardware environments.
In this role, you will:
- Build and maintain MLOps pipelines: Develop robust CI/CD for ML, model registries, and automated deployment workflows to support rapid iteration.
- Optimize for performance: Profile and benchmark models across cloud GPUs and edge devices (e.g., Nsight, PyTorch Profiler) to identify bottlenecks and implement hardware acceleration.
- Scale deployment: Design and implement model deployment strategies for both Cloud and Edge environments, ensuring efficient, low-latency execution in game runtimes.
- Enhance model efficiency: Apply precision tuning and quantization techniques to meet latency, cost, and memory constraints without significant quality loss.
- Collaborate on integration: Work with game engineers to integrate ML models into game engine pipelines and APIs.
About You
- MLOps & Deployment Expertise: Proven experience with model registries, containerization, and building end-to-end CI/CD pipelines for machine learning. Experience productionizing ML models in the cloud (e.g., AWS and SageMaker endpoints), including scaling, monitoring, and working closely with platform/infra teams.
- Hardware Profiling & Acceleration: Experience in profiling and optimizing ML inference on GPUs, with knowledge of CUDA-based runtimes and tools (e.g., Nsight, cuDNN, TensorRT, ONNX Runtime).
- Compiler & Runtime Knowledge: Familiarity with graph compiler optimization and tools like MLIR or LLVM.
- Framework Proficiency: Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong Software Engineering: Ability to develop high-quality, maintainable code and integrate complex algorithmic solutions into production systems.
- Passion for Games: A strong interest in how technology enables joy and innovation in the video game industry.
Bonus Experience
- Hands-on experience deploying ML models on edge, such as iOS or Android devices, including model optimization and hardware-aware inference.
- Experience in game development and familiarity with game engines (e.g., Unity, Unreal).
- Experience in model distillation, pruning, or other model compression techniques.
Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $466,000.00 - $750,000.00.
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.
Netflix is a unique culture and environment. Learn more here.
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do 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.

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next.
The Team
The Studio Media Algorithms team is at the forefront of algorithmic innovation to enhance and support the creation of Netflix’s entertainment content, including games. In this role, you will be embedded within this team while collaborating very closely with a specialized Games Studio R&D team. This incubation-style team is chartered to lead our investments in building new kinds of games leveraging emerging technologies to support our creators and reach player audiences in new ways.
The Role
We are looking for a Machine Learning Engineer with a focus on MLOps, deployment, and performance optimization to help bridge the gap between research and production in the gaming space. You will work cross-functionally with games technical directors, designers, and scientists to ensure that novel AI-driven game concepts can be deployed efficiently across a variety of hardware environments.
In this role, you will:
- Build and maintain MLOps pipelines: Develop robust CI/CD for ML, model registries, and automated deployment workflows to support rapid iteration.
- Optimize for performance: Profile and benchmark models across cloud GPUs and edge devices (e.g., Nsight, PyTorch Profiler) to identify bottlenecks and implement hardware acceleration.
- Scale deployment: Design and implement model deployment strategies for both Cloud and Edge environments, ensuring efficient, low-latency execution in game runtimes.
- Enhance model efficiency: Apply precision tuning and quantization techniques to meet latency, cost, and memory constraints without significant quality loss.
- Collaborate on integration: Work with game engineers to integrate ML models into game engine pipelines and APIs.
About You
- MLOps & Deployment Expertise: Proven experience with model registries, containerization, and building end-to-end CI/CD pipelines for machine learning. Experience productionizing ML models in the cloud (e.g., AWS and SageMaker endpoints), including scaling, monitoring, and working closely with platform/infra teams.
- Hardware Profiling & Acceleration: Experience in profiling and optimizing ML inference on GPUs, with knowledge of CUDA-based runtimes and tools (e.g., Nsight, cuDNN, TensorRT, ONNX Runtime).
- Compiler & Runtime Knowledge: Familiarity with graph compiler optimization and tools like MLIR or LLVM.
- Framework Proficiency: Extensive experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Strong Software Engineering: Ability to develop high-quality, maintainable code and integrate complex algorithmic solutions into production systems.
- Passion for Games: A strong interest in how technology enables joy and innovation in the video game industry.
Bonus Experience
- Hands-on experience deploying ML models on edge, such as iOS or Android devices, including model optimization and hardware-aware inference.
- Experience in game development and familiarity with game engines (e.g., Unity, Unreal).
- Experience in model distillation, pruning, or other model compression techniques.
Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $466,000.00 - $750,000.00.
Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.
Netflix is a unique culture and environment. Learn more here.
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do 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.
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Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at Netflix Jobs
Align your portfolio with Netflix's ML stack
Netflix engineering blog posts detail the systems behind their recommendation engine, A/B testing platform, and content encoding pipelines. Framing your portfolio around similar domains, such as ranking models or real-time inference, signals direct relevance to their open ML roles.
Target roles that clear the specialty occupation bar
USCIS requires H-1B roles to qualify as specialty occupations requiring a specific bachelor's degree or higher. ML Engineer positions at Netflix typically map to computer science, statistics, or a related technical field, so ensure your degree field is explicitly documented in your resume and application materials.
Use Migrate Mate to filter Netflix ML openings by visa type
Netflix posts ML roles across multiple teams with different sponsorship profiles. Use Migrate Mate to surface active Netflix listings filtered by the visa types you need, so you're applying to positions where sponsorship is already confirmed rather than guessing from a standard job board.
Prepare for Netflix's systems design interview format
Netflix ML interviews typically include a systems design round focused on building scalable recommendation or personalization infrastructure. Preparing examples where you've designed or optimized production ML pipelines strengthens your case and moves you faster to the offer stage where sponsorship discussions happen.
Request your LCA details before accepting an offer
Before signing, ask your recruiter to confirm the job location listed on the Labor Condition Application filed with DOL. Netflix operates across multiple offices, and the LCA must reflect your actual work site or remote arrangement to avoid compliance issues after you start.
Plan around the H-1B cap if you're on OPT
If you're currently on F-1 OPT, Netflix would need to file your H-1B cap petition in April for an October 1 start date. STEM OPT extensions give you up to 24 additional months of work authorization, so timing your Netflix application to maximize that window reduces gaps in your authorization.
Machine Learning Engineer at Netflix jobs are hiring across the US. Find yours.
Find Machine Learning Engineer at Netflix JobsFrequently Asked Questions
Does Netflix sponsor H-1B visas for Machine Learning Engineers?
Yes, Netflix sponsors H-1B visas for Machine Learning Engineer roles. ML engineering positions at Netflix typically qualify as specialty occupations under USCIS criteria, given the degree requirements in computer science, statistics, or a closely related field. Netflix handles sponsorship through its internal immigration team, so the process is well-established for technical hires rather than being managed ad hoc.
Which visa types does Netflix commonly sponsor for Machine Learning Engineer roles?
Netflix sponsors several visa categories for ML engineering talent, including H-1B, E-3 for Australian citizens, TN for Canadian and Mexican nationals, and F-1 OPT and CPT for students. For longer-term pathways, Netflix also supports Green Card sponsorship through EB-2 and EB-3 classifications, making it a viable option if you're thinking beyond your first work visa.
What qualifications does Netflix expect for Machine Learning Engineer roles?
Netflix ML Engineer roles typically require a bachelor's or advanced degree in computer science, mathematics, or statistics, along with hands-on experience building production ML systems rather than purely research work. Familiarity with large-scale recommendation systems, real-time inference pipelines, or experimentation platforms aligns well with Netflix's known technical priorities. Industry experience matters more at Netflix than academic credentials alone.
How do I apply for Machine Learning Engineer jobs at Netflix?
You can find and filter active Netflix Machine Learning Engineer openings by visa type on Migrate Mate, which lets you confirm sponsorship availability before applying. When applying directly, tailor your resume to highlight production ML experience over research projects, since Netflix engineering interviews focus heavily on systems thinking and real-world model deployment rather than theoretical knowledge.
How do I plan my timeline if Netflix sponsors my visa?
If you're on F-1 OPT, STEM OPT gives you up to 24 additional months of work authorization, which gives Netflix time to file an H-1B cap petition by the April deadline for an October 1 start. For E-3 or TN holders, Netflix can typically file outside the cap with shorter lead times. Confirm your start date and visa category with Netflix's immigration team early so USCIS processing timelines don't delay your onboarding.
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