Senior / Staff Machine Learning Engineer
Join a team at the forefront of ML infrastructure and generative AI, where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. We focus on building robust systems that connect scalable data pipelines with advanced ML workflows to accelerate the development of real-world AI applications.
Description
The ADP ML Data Platform team enables future Apple intelligent products by providing Apple engineers with cutting edge ML technologies, large scale compute and data systems specifically designed for machine learning.
You will build the data foundation that powers ML training across Apple. Our team enables governed, scalable sharing of text and multimodal datasets, ensuring teams can safely discover, access, and use high-quality data for training.
We focus on turning raw data into usable training assets with streamlining data preparation, enabling rapid iteration, and supporting advanced techniques such as synthetic data workflows. Our goal is to remove friction between data creation and model experimentation so teams can move from idea to training quickly and confidently.
Most critically, we optimize how data is consumed during training. We work on improving GPU utilization and reducing training bottlenecks through deep benchmarking, profiling, and system-level optimization of data pipelines. This includes designing high-performance data access patterns for large-scale distributed workloads and ensuring reliability and efficiency at scale.
You will operate at the intersection of ML systems and infrastructure, partnering with model teams to improve end-to-end training performance, eliminate inefficiencies, and raise the bar on reproducibility and governance. We are looking for engineers with strong experience in large-scale training systems, performance optimization, and data-intensive ML workloads. If you care about maximizing efficiency, designing scalable data architectures, and enabling the next generation of generative AI models, this role offers the scope and impact to do exactly that.
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","responsibilities":"As a member of the Apple ML Data Platform team, your responsibilities will include:
Design and build the ML data platform that enables governed discovery, sharing, and access to large-scale text and multimodal datasets across the company
Develop systems that transform raw and synthetic data into high-quality, training-ready assets, enabling rapid experimentation and iteration
Architect and optimize high-performance data pipelines for large-scale distributed training, improving GPU utilization and reducing end-to-end training time
Benchmark and profile training workloads to identify data bottlenecks, and implement system-level optimizations to eliminate them
Partner with model and research teams to improve reproducibility, reliability, and performance of training workflows
Build scalable infrastructure that supports foundation models and next-generation ML workloads with strong guarantees around lineage, versioning, and compliance
Preferred Qualifications
Drive platform-wide improvements in data efficiency, resilience, and observability across distributed environments
Diagnose and resolve complex cross-stack performance issues, from data ingestion through training execution, ensuring reliability at scale
Minimum Qualifications
Strong foundation in machine learning systems, with hands-on experience in large-scale training workflows and data-intensive ML pipelines
Deep understanding of training performance optimization, including profiling, benchmarking, and eliminating data bottlenecks in distributed environments
Experience building production-grade ML data or training infrastructure with strong guarantees around reproducibility, versioning, and governance
Proven ability to design high-throughput, low-latency data pipelines for large-scale GPU workloads
Familiarity with modern foundation models and multimodal training workloads
Experience operating and debugging distributed systems in large-scale production environments
Strong systems programming skills in Python and at least one of Java or Go
Ability to work cross-functionally with research, infrastructure, and product teams to improve end-to-end ML performance
Comfortable operating in fast-moving, ambiguous problem spaces with evolving technical requirements
B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .
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 $181,100 and $318,400, 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. Learn more about Apple Benefits.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Perks and Benefits
Health and Wellness
Parental Benefits
Work Flexibility
Office Life and Perks
Vacation and Time Off
Financial and Retirement
Professional Development
Diversity and Inclusion
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