Senior ML Engineer Jobs at Apple with Visa Sponsorship
Senior ML Engineer jobs at Apple involve building some of the company's most ambitious machine learning infrastructure across on-device intelligence, Siri, computer vision, and silicon optimization. Apple has a strong track record of sponsoring work visas across multiple categories for qualified ML engineers, making it a viable path for international candidates.
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Get Access To All JobsINTRODUCTION
The Apple Ray team is seeking a Senior / Staff Software Engineer with strong distributed systems expertise and a solid background in machine learning. In this hybrid role, you will design and build core components of Apple’s unified data+ML platform powered by open-source Ray, while also partnering with ML teams to ensure the platform meets the needs of large-scale training and inference workloads.
You will contribute to the distributed runtime, orchestration layer, and system APIs that power Apple’s intelligent features across products and services. This role is ideal for a software engineer who enjoys low-level systems work but is also fluent in ML workflows and models at scale.
Description
Apple Ray integrates deeply with Apple’s data and ML ecosystem to provide a unified platform for building, orchestrating, and scaling complex ML and data pipelines. As a Software Engineer with ML background, you will design distributed systems that support large-scale model training, tuning, and inference across heterogeneous compute environments—from bare-metal GPU clusters to cloud-native infrastructure.
You will build features that enhance developer productivity for ML engineers, improve resource efficiency, and advance the performance and reliability of Apple’s ML workloads. You’ll collaborate closely with ML practitioners to translate model and pipeline needs into robust platform capabilities, while also improving the underlying distributed runtime and control plane.
This role requires strong engineering fundamentals, hands-on experience with ML systems, and a passion for building scalable infrastructure.
Responsibilities
-
Build scalable distributed systems and platform components using Ray that power Apple’s data+ML workflows.
-
Develop APIs, libraries, and services that improve the efficiency and usability of large-scale ML training and inference pipelines.
-
Optimize performance and resource utilization across GPU/CPU clusters for ML workloads running at Apple scale.
-
Collaborate with ML teams to understand model and pipeline needs and translate them into robust platform features.
-
Design fault-tolerant orchestration mechanisms, autoscaling strategies, and runtime improvements for distributed ML jobs.
-
Diagnose complex issues across distributed systems and ML pipelines to ensure reliability and availability.
-
Improve observability, monitoring, and debugging capabilities targeted at ML-centric distributed workloads.
-
Contribute to architectural decisions and, where appropriate, upstream enhancements to Ray and related tools.
MINIMUM QUALIFICATIONS
-
6+ years building distributed systems, high-scale backend services, or compute runtimes.
-
Solid background in ML workflows, model training, model serving, or data pipeline development.
-
Proficiency in Python, plus strong experience in a systems-level language (C++, Rust, Go, or Java).
-
Experience with ML frameworks such as PyTorch or TensorFlow and familiarity with GPU-based training.
-
Understanding of parallelism strategies, model scaling, or distributed training concepts.
-
Experience with cluster orchestration (Kubernetes, EKS, GKE) or large-scale compute systems.
-
Strong debugging skills across distributed and ML-centric runtime environments.
-
Ability to work cross-functionally with ML engineers, data engineers, and infrastructure teams.
-
B.S., M.S., or Ph.D. in Computer Science, Machine Learning, or related technical fields - or comparable software engineering experience.
PREFERRED QUALIFICATIONS
-
Experience with distributed training frameworks (DeepSpeed, Horovod, FSDP, ZeRO).
-
Background in optimizing GPU workloads or performance benchmarking.
-
Experience with model orchestration systems or ML platforms.
-
Contributions to open-source ML or distributed systems projects.
-
Familiarity with large-scale data systems such as Spark, Flink, or similar.
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
INTRODUCTION
The Apple Ray team is seeking a Senior / Staff Software Engineer with strong distributed systems expertise and a solid background in machine learning. In this hybrid role, you will design and build core components of Apple’s unified data+ML platform powered by open-source Ray, while also partnering with ML teams to ensure the platform meets the needs of large-scale training and inference workloads.
You will contribute to the distributed runtime, orchestration layer, and system APIs that power Apple’s intelligent features across products and services. This role is ideal for a software engineer who enjoys low-level systems work but is also fluent in ML workflows and models at scale.
Description
Apple Ray integrates deeply with Apple’s data and ML ecosystem to provide a unified platform for building, orchestrating, and scaling complex ML and data pipelines. As a Software Engineer with ML background, you will design distributed systems that support large-scale model training, tuning, and inference across heterogeneous compute environments—from bare-metal GPU clusters to cloud-native infrastructure.
You will build features that enhance developer productivity for ML engineers, improve resource efficiency, and advance the performance and reliability of Apple’s ML workloads. You’ll collaborate closely with ML practitioners to translate model and pipeline needs into robust platform capabilities, while also improving the underlying distributed runtime and control plane.
This role requires strong engineering fundamentals, hands-on experience with ML systems, and a passion for building scalable infrastructure.
Responsibilities
-
Build scalable distributed systems and platform components using Ray that power Apple’s data+ML workflows.
-
Develop APIs, libraries, and services that improve the efficiency and usability of large-scale ML training and inference pipelines.
-
Optimize performance and resource utilization across GPU/CPU clusters for ML workloads running at Apple scale.
-
Collaborate with ML teams to understand model and pipeline needs and translate them into robust platform features.
-
Design fault-tolerant orchestration mechanisms, autoscaling strategies, and runtime improvements for distributed ML jobs.
-
Diagnose complex issues across distributed systems and ML pipelines to ensure reliability and availability.
-
Improve observability, monitoring, and debugging capabilities targeted at ML-centric distributed workloads.
-
Contribute to architectural decisions and, where appropriate, upstream enhancements to Ray and related tools.
MINIMUM QUALIFICATIONS
-
6+ years building distributed systems, high-scale backend services, or compute runtimes.
-
Solid background in ML workflows, model training, model serving, or data pipeline development.
-
Proficiency in Python, plus strong experience in a systems-level language (C++, Rust, Go, or Java).
-
Experience with ML frameworks such as PyTorch or TensorFlow and familiarity with GPU-based training.
-
Understanding of parallelism strategies, model scaling, or distributed training concepts.
-
Experience with cluster orchestration (Kubernetes, EKS, GKE) or large-scale compute systems.
-
Strong debugging skills across distributed and ML-centric runtime environments.
-
Ability to work cross-functionally with ML engineers, data engineers, and infrastructure teams.
-
B.S., M.S., or Ph.D. in Computer Science, Machine Learning, or related technical fields - or comparable software engineering experience.
PREFERRED QUALIFICATIONS
-
Experience with distributed training frameworks (DeepSpeed, Horovod, FSDP, ZeRO).
-
Background in optimizing GPU workloads or performance benchmarking.
-
Experience with model orchestration systems or ML platforms.
-
Contributions to open-source ML or distributed systems projects.
-
Familiarity with large-scale data systems such as Spark, Flink, or similar.
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $147,400 and $272,100, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
See all 79+ Senior ML Engineer Jobs at Apple
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Get Access To All JobsTips for Finding Senior ML Engineer Jobs at Apple
Align your portfolio to Apple's ML domains
Apple's ML hiring concentrates on on-device inference, CoreML, neural engine optimization, and privacy-preserving techniques. Frame past projects around model efficiency and deployment constraints, not just research accuracy, before you apply.
Target teams beyond Siri and Photos
Apple's Health, Maps, and silicon teams actively hire Senior ML Engineers and are less saturated with applicants. Roles tied to Apple Silicon's Neural Engine often require deeper systems knowledge, which narrows the field in your favor.
Confirm your visa category before accepting an offer
Apple sponsors H-1B, E-3, TN, and F-1 OPT among others, but the right category depends on your nationality and status. Clarify which petition Apple's immigration team will file before you negotiate, since each category carries different timelines.
Start the H-1B lottery registration early in your job search
If you need H-1B sponsorship, USCIS registration opens in March for an October 1 start date. Targeting Apple roles in Q4 of the prior year gives your recruiter enough runway to register you in that cycle.
Request premium processing if your OPT window is tight
F-1 OPT gives you a 60-day grace period after employment ends. If Apple's offer timeline is running close to your OPT expiration, ask the recruiting team whether USCIS premium processing is available for your petition category.
Use Migrate Mate to filter Apple's open Senior ML Engineer roles by visa type
Sponsorship eligibility varies by role and team at Apple. Use Migrate Mate to surface Senior ML Engineer postings at Apple filtered by the visa categories you qualify for, so you apply where sponsorship is already confirmed.
Frequently Asked Questions
Does Apple sponsor H-1B visas for Senior ML Engineers?
Yes, Apple sponsors H-1B visas for Senior ML Engineers. Apple participates in the annual USCIS H-1B lottery registration each March and files cap-subject petitions for selected candidates. If you're already in H-1B visa status with another employer, Apple can also file an H-1B transfer, which lets you start work once the petition is received by USCIS, without waiting for a full approval.
How do I apply for Senior ML Engineer jobs at Apple?
Applications go through Apple's careers portal at jobs.apple.com. Senior ML Engineer roles at Apple typically require you to pass a recruiter screen, a technical phone interview focused on ML fundamentals and system design, and a full loop of four to six interviews covering modeling, coding, and cross-functional collaboration. You can also browse open roles filtered by visa sponsorship eligibility on Migrate Mate before applying directly.
Which visa types does Apple commonly sponsor for Senior ML Engineers?
Apple sponsors several visa categories for Senior ML Engineers depending on your nationality and current status. H-1B and Green Card sponsorship through EB-2 or EB-3 PERM are available for most nationalities. Australian citizens can pursue the E-3 visa, which has no lottery. Canadian and Mexican nationals may qualify for TN visa status. F-1 OPT and CPT are also supported, covering recent graduates in active OPT periods.
What qualifications does Apple expect for Senior ML Engineer roles?
Apple's Senior ML Engineer postings consistently require a graduate degree in machine learning, computer science, or a related field, alongside hands-on experience deploying models in production environments. Proficiency in Python and a deep learning framework like PyTorch or JAX is expected. Roles tied to Apple Silicon or on-device inference also expect familiarity with model quantization, pruning, and hardware-aware optimization, which goes beyond typical research-focused ML backgrounds.
How do I think about timing if I need Apple to sponsor my visa?
Timing depends on your current status. H-1B cap-subject petitions have a fixed annual cycle, so an offer in late spring may mean waiting until October 1 for your start date. E-3 visa and TN sponsorship can move faster since neither requires lottery selection. PERM-based Green Card sponsorship runs in parallel with employment and takes significantly longer. Discuss your status with Apple's immigration team during the offer stage, not after signing.