ML Platform Engineer

Apple • Sunnyvale, CA

16h

About The Position

We're starting to see the incredible potential of multimodal foundation and
large language models, and many applications in the computer vision and machine
learning domain that previously appeared infeasible are now within reach. We are
looking for highly motivated and skilled Machine Learning Platform Engineers to
join our team in the VCV group and help us enable that potential for realtime
human understanding on Apple devices. The VCV org has pioneered human-centric
real-time features such as FaceID, FaceKit, and Gaze and Hand gesture control
which have changed the way millions of users interact with their devices. We
balance research and product requirements to deliver Apple quality, pioneering
experiences, innovating through the full stack, and partnering with HW, SW and
AI teams to shape Apple's products and bring our vision to life. Join us to
build the infrastructure, MLOps platforms, and deployment systems that power
Apple's next generation of intelligent products and experiences.

DESCRIPTION

As part of the VCV team, you will build and maintain the critical infrastructure
that enables machine learning at scale across Apple's products. You will work on
infrastructure, MLOps, cloud and on-device deployment systems, and data
engineering platforms that support our ML development lifecycle. You will be
responsible for building and maintaining scalable machine learning
infrastructure for training, evaluation, and deployment of computer vision and
multimodal models. You will develop MLOps platforms and tools that streamline
the ML development lifecycle from data ingestion to model deployment, create
robust data pipelines for large-scale data collection, curation, preprocessing,
and management, and implement on-device ML integration systems that deploy
state-of-the-art algorithms to Apple devices. Working closely with ML algorithms
engineers, data scientists, and quality assurance teams, you'll help deploy
state-of-the-art computer vision technologies on Apple devices, balancing
performance with the compute and power constraints of on-device inference.

Requirements

• Bachelor's degree in Computer Science, Software Engineering, or related technical field, or equivalent practical experience
• 2+ years of relevant industry experience in software engineering, machine learning infrastructure, or related fields
• Strong programming skills in Python, C++, and/or Swift
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX
• Knowledge of machine learning model development lifecycle, including data preprocessing, model training, evaluation, and deployment
• Experience with distributed systems, cloud computing, or large-scale data processing
• Strong foundational knowledge in Computer Science and software engineering principles

Nice To Haves

• Master's degree in Computer Science, Machine Learning, or related technical field
• 2+ years of experience in ML infrastructure, platform engineering, or production ML systems
• Experience with Apple's frameworks including CoreFoundation, RealityKit, and CoreML
• Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code
• Experience with containerization technologies (Docker, Kubernetes) and orchestration systems
• Knowledge of cloud platforms (AWS, GCP, Azure) and distributed computing frameworks (Spark, Ray, etc.)
• Experience with GPU programming and hardware acceleration (Metal, CUDA, OpenCL)

Responsibilities

• building and maintaining scalable machine learning infrastructure for training, evaluation, and deployment of computer vision and multimodal models
• develop MLOps platforms and tools that streamline the ML development lifecycle from data ingestion to model deployment
• create robust data pipelines for large-scale data collection, curation, preprocessing, and management
• implement on-device ML integration systems that deploy state-of-the-art algorithms to Apple devices
• deploy state-of-the-art computer vision technologies on Apple devices, balancing performance with the compute and power constraints of on-device inference

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees