Applied AI Scientist

Apple Apple · Big Tech · Culver City +2 · Machine Learning and AI

This Applied AI Scientist role at Apple focuses on building and deploying scalable ML and AI solutions to enhance product intelligence and automation, particularly for creative applications like video editing. The role involves end-to-end ML pipeline development, prototyping AI features, and collaborating with engineering and product teams to bring intelligent solutions to production. It requires strong experience in statistical modeling, machine learning algorithms, large-scale data systems, and deploying models into production, with a preference for generative AI, computer vision, and LLM experience.

What you'd actually do

  1. Designing, developing, and implementing sophisticated machine learning and AI models to solve complex problems, particularly for creative applications like our video editing apps.
  2. Building end-to-end ML pipelines, prototyping novel AI-powered features, developing AI tools, and collaborating closely with engineering, product, and marketing partners to bring intelligent solutions into production.
  3. Hands-on experience deploying AI/ML models into a production environment.
  4. Experience developing or contributing to AI frameworks, APIs, or internal tools used by other teams.
  5. Proven ability to translate complex research ideas into scalable, production-level AI solutions.

Skills

Required

  • Python
  • Pandas
  • Scikit-learn
  • NumPy
  • statistical modeling
  • machine learning algorithms
  • supervised learning
  • unsupervised learning
  • classification
  • regression
  • clustering
  • large-scale data
  • distributed systems
  • Hadoop
  • Spark
  • deep learning algorithms
  • CNN
  • RNN
  • Transformers
  • TensorFlow
  • PyTorch
  • causal inference models
  • deploying AI/ML models into production
  • rapid prototyping
  • reproduction
  • validation of research ideas
  • AI frameworks
  • APIs
  • internal tools
  • analytical skills
  • communication skills
  • collaboration skills

Nice to have

  • LLM fine-tuning
  • prompt engineering
  • retrieval-augmented generation (RAG)
  • generative AI
  • diffusion models
  • computer vision (CV)
  • multimodal models
  • video generation/understanding
  • video analysis
  • image analysis
  • cloud platforms
  • AWS
  • GCP
  • Azure
  • MLOps tools
  • SageMaker
  • Vertex AI
  • MLflow
  • user behavior analysis
  • content usage analysis
  • feature analytics
  • product insights
  • production model monitoring
  • data drift
  • model drift
  • diagnostics
  • scalable AI/ML systems
  • software engineering practices
  • version control
  • testing
  • code review
  • cross-domain applications of AI/ML
  • marketing analytics
  • personalization
  • recommendation systems

What the JD emphasized

  • PhD in Computer Science, Statistics, Mathematics, or a related quantitative field with 3+ years of relevant experience; or MS with 5+ years of experience in applied AI, machine learning, or statistical modeling.
  • 3+ years of programming proficiency with Python for data science and AI (e.g., Pandas, Scikit-learn, NumPy).
  • 3+ years of hands-on experience applying statistical modeling and machine learning algorithms for supervised and unsupervised learning (classification, regression, clustering, etc.).
  • 3+ years of experience working with large-scale data and distributed systems (e.g., Hadoop, Spark).
  • Working familiarity with deep learning algorithms (CNN, RNN, Transformers) and frameworks (TensorFlow, PyTorch).
  • Working familiarity with causal inference models and techniques.
  • Hands-on experience deploying AI/ML models into a production environment.
  • Experience with rapid prototyping, reproduction, and validation of research ideas.
  • Experience developing or contributing to AI frameworks, APIs, or internal tools used by other teams.

Other signals

  • building scalable ML and AI solutions
  • enhance our product intelligence
  • improve automation
  • expand our AI-driven capabilities
  • designing, developing, and implementing sophisticated machine learning and AI models
  • building end-to-end ML pipelines
  • prototyping novel AI-powered features
  • developing AI tools
  • bring intelligent solutions into production
  • deploy AI/ML models into a production environment
  • translate complex research ideas into scalable, production-level AI solutions