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Applied Machine Learning Engineer, Apple Intelligence

Apple

Bengaluru

5+ years

Today

$73.5K–95.2K/yr

Full-time

Onsite

Skills Required

LLM

RAG

Gen AI

Claude

Multi-agent systems

Cursor

Codex

Tokenization

Language Modeling

Decoding

Classification

Python

C++

Objective-C

Description

Apple is seeking an Applied ML Engineer to work on generative AI systems impacting billions of users. The role involves applying ML skills and engineering rigor to build scalable, production-grade AI solutions.

Company: Apple

Role: Applied ML Engineer

Experience

5+ years of hands-on ML experience building harnesses and integrating models into production workflows
Experience staging, provisioning, or controlling test or evaluation environments
Experience evaluating ML, LLM or agent-based systems
Experience building, maintaining, or operating production-grade AI agents at scale

Responsibilities

Architect and deliver scalable, production-grade AI solutions
Apply and integrate generative modeling techniques into production systems
Own components across the pipeline from data preprocessing to evaluation and deployment
Ensure systems are culturally and linguistically adaptable across local experiences
Build generative AI systems that reach real users at scale
Ship clean, well-tested, maintainable code
Participate in code reviews, architectural discussions, and production troubleshooting

Additional Responsibilities

Work alongside a dedicated team of ML and software engineers
Transition smoothly across changing platforms, frameworks, and components
Communicate effectively including sharing knowledge and elevating quality across the team
Focus on debugging and performance
Drive results in a fast-paced environment with a commitment to excellence

Nice To Have

Background in developer tools, internal platforms, or productivity engineering
Strong problem-solving skills with a focus on what matters most
Experience with metrics, scoring methodology, or trajectory and outcome analysis for ML systems

More Skills

Applied machine learning, Generative AI systems, Swift, OS fundamentals, OOPS, PyTorch, TensorFlow, NLP (tokenization, language modeling, decoding, classification), Modern agentic and multi-agent systems, Generative-AI coding tools (Claude, Cursor, Codex), Debugging, Systems engineering, Software development lifecycle methodologies

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Applied Machine Learning Engineer, Apple Intelligence

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