Manager, Machine Learning Infrastructure - SIML at Apple

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

06 Aug, 26

Salary

0.0

Posted On

08 May, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning Infrastructure, Distributed Systems, System Design, Python, Java, Go, C/C++, Data Pipelines, Large Language Models, Computer Vision, Natural Language Processing, Backend Infrastructure, Technical Leadership, Model Evaluation, Scalable Systems, Compute Efficiency

Industry

Computers and Electronics Manufacturing

Description

Do you think Computer Vision and Machine Learning can change the world? Do you think it can transform the way millions of people collect, discover and share the most special moments of their lives? We truly believe it can. And we are looking for hardworking engineers who can contribute to building the ecosystem of tooling necessary to create these exciting technologies. We are the System Intelligent and Machine Learning (SIML) group that provides foundational computer vision and machine learning technologies to Apple's ecosystem. Our work is behind essential features such as Camera, Text & Handwriting recognition, and Apple Intelligence experiences (Image Playground, Writing Tools, Smart Script, Math Notes..). We are seeking an Engineering Manager to lead the development of scalable, high-performance infrastructure that powers product-focused machine learning initiatives. DESCRIPTION In this role you will lead a team responsible for building and operating infrastructure that enables large-scale data processing (terabytes and beyond) across domains such as image generation, large language models (LLMs), computer vision, natural language processing, human-computer interaction, and text recognition. This includes designing systems for dataset creation and management, ingesting annotated and inferred data, and delivering seamless access to high-quality data for ML researchers and engineers. A key part of this role is driving systems that enable deeper understanding of model behavior—such as failure mode analysis, evaluation pipelines, and benchmarking frameworks—to accelerate iteration velocity and improve model quality. You will work across the stack, tackling challenges ranging from low-level distributed systems and compute efficiency to building stable, intuitive interfaces for internal users. As a leader, you will partner closely with cross-functional teams including ML researchers, product teams, and platform engineering to define roadmaps, align priorities, and deliver impactful solutions. You will play a critical role in shaping how ML systems are developed, evaluated, and scaled from early experimentation to production. MINIMUM QUALIFICATIONS Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer Engineering, or a related field (or equivalent experience) 7+ years of software engineering experience, with 2+ years in a technical leadership or management role Strong programming skills in one or more of: Python, Java, Go, C/C++ Solid understanding of machine learning fundamentals and ML system workflows Proven experience in building and scaling distributed systems and backend infrastructure Strong system design skills with expertise in at least one systems domain (e.g., data infrastructure, distributed systems, ML platforms) PREFERRED QUALIFICATIONS Experience building infrastructure for ML workflows (data pipelines, training systems, evaluation frameworks, or deployment systems) Domain experience in areas such as AI/ML, computer vision, NLP, or related fields Experience working with large-scale datasets and compute-intensive systems Experience improving developer productivity through tooling and platform abstractions Ability to operate effectively in cross-functional, fast-paced environments with evolving requirements

Responsibilities

Lead a team in building and operating scalable infrastructure for large-scale data processing and ML workflows. Partner with cross-functional teams to define roadmaps and develop systems for model evaluation and benchmarking.