Junior Machine Learning Engineer / Junior ML Engineer

Job Description

About the Role

Apple is seeking a Junior Machine Learning Engineer (2+ years experience) to contribute to building highquality ML models, data processing pipelines, and scalable ML systems used across Apple products. This role blends handson model development with ML pipelines, data engineering workflows, and practical deployment of ML solutions aligned with Apple s emphasis on scalable, productionready ML systems.

The ideal candidate is comfortable working with deep learning frameworks, Pythonbased ML development, and collaborating in fastpaced engineering environments.

Key Responsibilities

• Build, train, and evaluate ML models using Python, TensorFlow, PyTorch, and scikitlearn.
• Develop and maintain ML pipelines and data processing workflows, supporting model training, evaluation, and deployment at scale.
• Work with distributed data systems to support reliable model experimentation, data preparation, and inference workflows.
• Collaborate with crossfunctional teams including ML researchers, data engineers, and software engineers to deliver highimpact ML features.
• Support endtoend ML lifecycle: data gathering, feature engineering, model training, evaluation, deployment, monitoring, and iteration.

Required Skills & Qualifications

• 3+ years of industry experience in machine learning or applied ML engineering.
• Strong programming experience in Python and proficiency with ML libraries such as TensorFlow, PyTorch, scikitlearn.
• Experience with ML model development, including supervised/unsupervised learning, model evaluation, and experimentation.
• Handson experience with ML pipelines, data preprocessing, and scalable data systems (e.g., Spark, Ray, or similar).
• Ability to write productionquality, efficient, maintainable ML code.
• Strong analytical, problemsolving, and communication skills.
  

Preferred Qualifications

• Familiarity with distributed ML training, GPUbased model optimization, or largescale experimentation.
• Experience integrating ML into real products or features.
• Understanding of data lifecycle workflows, annotation pipelines, or evaluation frameworks.
• Exposure to computer vision, NLP, or multimodal ML systems (not required but a plus).