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AIML - Sr. Machine Learning Infrastructure Engineer, Evaluation
AIML - Sr. Machine Learning Infrastructure Engineer, EvaluationApple Inc. • San Francisco, CA, United States
AIML - Sr. Machine Learning Infrastructure Engineer, Evaluation

AIML - Sr. Machine Learning Infrastructure Engineer, Evaluation

Apple Inc. • San Francisco, CA, United States
6 days ago
Job type
  • Full-time
Job description

AIML - Sr. Machine Learning Infrastructure Engineer, Evaluation

San Francisco, California, United States Software and Services

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 iPhone 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 Staff 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.

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.
  • 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.
  • 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.

    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.

    Apple accepts applications to this posting on an ongoing basis.

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    Sr Machine Learning Engineer • San Francisco, CA, United States

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