About the job
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.
Responsibilities
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.
Qualifications
Minimum
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
Preferred
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)