招聘
How do we ensure that Apple's most advanced AI features perform flawlessly for
everyone, everywhere? At Apple, the AI/ML Evaluation team answers this question.
We are the architects of quality and trust for AI across all Apple products, from Siri to
the i Phone camera. We build the systems and methodologies that rigorously test our
models against the complexity and diversity of the real world, ensuring they are robust, fair, and deliver a magical experience.
To truly challenge our models, we must go beyond existing data. This is where you
come in. We are looking for a staff engineer to lead the creation of a groundbreaking
tooling for synthetic data generation. You will architect the systems that create vast,
diverse datasets - spanning text, images, video, and audio to simulate the edge
cases and future scenarios our models will encounter. Your work will be the foundation for a new paradigm of AI evaluation at Apple.
Description
As a Senior Engineer on the AI/ML Evaluation team, you will lead the design and implementation of a platform dedicated to generating high-fidelity synthetic data at an unprecedented scale. You will be responsible for the end-to-end infrastructure that powers a new generation of generative models, enabling us to create realistic and challenging text, image, audio, and video content. This platform is critical to our mission of ensuring Apple's AI is the most reliable and trustworthy in the world. You will be a key technical leader, setting the strategy for how we build, deploy, and leverage generative AI for evaluation.","responsibilities":"Architect a highly scalable, multi-modal platforms for generating synthetic data using the latest generative models.
Design and build robust, high-performance microservices in Golang to serve as the backbone of the data generation platform.
Operate and scale distributed compute infrastructure, including large Apple internal job scheduling environments and dedicated GPU clusters.
Develop resilient data pipelines for the curation, processing, and management of massive synthetic datasets.
Define the technical strategy for integrating synthetic data into our core AI evaluation and testing workflows.
Collaborate closely with research scientists and ML engineers to develop novel generative models for evaluation purposes.
Optimize the computational efficiency and scheduling of data generation workloads, with a deep focus on maximizing GPU utilization.
Mentor engineers across the organization on best practices for building and scaling distributed systems for generative AI.
Preferred Qualifications
Direct experience architecting systems for training or running large-scale generative models (e.g., Diffusion Models, GANs, LLMs).
Familiarity with using synthetic data for model testing, validation, robustness checks, or fairness evaluation.
Architectural ownership of a large-scale ML platform or microservice-based system in a production environment.
Strategic leadership in defining technical roadmaps and influencing cross-functional teams in an ambiguous, fast-paced domain.
MS or PhD in Computer Science or a related field.
Minimum Qualifications
10+ years of professional software engineering experience building and operating large-scale, high-performance distributed systems.
Strong programming skills in Go and Python, with proven experience building production services.
Deep theoretical and practical knowledge of distributed systems principles (e.g., consensus, consistency, scalability).
Hands-on expertise with container orchestration and infrastructure-as-code in a production environment.
Experience designing and operating infrastructure for machine learning workloads on GPU compute.
BS in Computer Science or equivalent work 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 $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. 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.
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关于Apple

Apple
PublicApple Inc. is an American multinational technology company headquartered in Cupertino, California, in Silicon Valley, best known for its consumer electronics, software and online services.
10,001+
员工数
Cupertino
总部位置
$3.5T
企业估值
评价
3.9
10条评价
工作生活平衡
2.5
薪酬
4.2
企业文化
3.8
职业发展
3.5
管理层
3.2
72%
推荐给朋友
优点
Great benefits and compensation
Talented colleagues and supportive teams
Learning opportunities and mentorship
缺点
Work-life balance challenges
High stress and pressure
Fast-paced environment
薪资范围
11,365个数据点
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Principal/L7
Senior/L5
Staff/L6
Junior/L3 · Data Scientist ICT2
0份报告
$121,979
年薪总额
基本工资
-
股票
-
奖金
-
$103,682
$140,276
面试经验
3次面试
难度
3.3
/ 5
时长
28-42周
录用率
33%
体验
正面 33%
中性 0%
负面 67%
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
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