1,917 jobs for "Machine Learning Engineer"

At Apple, we believe in hard work, a fun environment, and the creativity and innovation that come from diverse perspectives. The people here at Apple don't just build products - they create the kind of wonder that revolutionizes entire industries. It's the diversity of those people and their ideas that inspires the innovation running through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it.

At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

This is a highly visible role supporting Apple's Sales organization in China. We are looking for a Data Scientist who can combine business understanding, analytical thinking, and applied AI capability to solve complex business problems and build scalable AI-enabled solutions.

This role is not a pure data science role or a pure machine learning engineering role. The ideal candidate should be able to understand ambiguous business needs, translate them into structured workflows, and design agent-based solutions that can support recurring decision-making processes. We are especially interested in candidates who have the imagination and big-picture thinking to orchestrate AI agents, manage multi-agent interactions, and turn business knowledge, data, and tools into reusable analytical products.

Description
Partner closely with business stakeholders to understand priorities, identify pain points, and uncover opportunities where data and AI can improve business decision-making.
Translate ambiguous business questions into structured analytical frameworks, agent workflows, and scalable solution designs.
Design and orchestrate AI agents that can perform tasks such as business Q&A, research, information extraction, data analysis, workflow automation, and decision support.
Define how multiple agents interact with each other, including task decomposition, role assignment, context sharing, tool usage, memory management, validation, and final response synthesis.
Convert recurring business processes into reusable analytics products, internal tools, or agent-based workflows.
Use data analysis, experimentation, forecasting, causal inference, optimization, LLMs, RAG, or other applied AI techniques to diagnose business issues and support decisions.
Work with engineering, data, product, and business teams to prototype, iterate, and productionize AI-enabled analytical solutions.
Communicate complex analytical and AI concepts clearly to both technical and non-technical audiences.
Help shape best practices for building reliable, scalable, and business-relevant AI agent solutions.

Preferred Qualifications
PhD, or Master's degree in Economics, Statistics, Data Science, Computer Science, Engineering, Operations Research, or a related quantitative / technical field.
Relevant experience in data science, applied AI, analytics, product analytics, strategy, consulting, machine learning, or AI product development.
Experience designing or building AI agents, multi-agent workflows, business copilots, research agents, Q&A agents, or automated analytical workflows.
Understanding of agent orchestration concepts such as planning, routing, tool use, memory, validation, reflection, handoff, and multi-agent collaboration.
Experience converting repeatable business needs into scalable tools, data products, or AI-enabled workflows.
Strong product sense and imagination, with the ability to think beyond one-off analysis and design reusable solutions.
Ability to operate with a big-picture mindset, connecting business strategy, data, AI capability, and execution feasibility.
Experience working with engineering, product, machine learning, or data platform teams to productionize analytical or AI solutions.
Familiarity with experimentation, causal inference, forecasting, segmentation, optimization, or measurement strategy is a plus.
Demonstrated business maturity, ownership, and delivered impact are valued over a fixed number of years of experience.

Minimum Qualifications
Strong quantitative, analytical, or technical background in data science, statistics, economics, operations research, computer science, engineering, or a related field; or equivalent practical experience.
Demonstrated capability in structured problem solving, analytical thinking, and using data or AI to address business problems.
Strong business understanding and problem-structuring ability, especially in ambiguous environments.
Proficiency in Python and SQL for data analysis, prototyping, and workflow development.
Familiarity with LLM applications, RAG, AI agents, tool calling, workflow automation, or applied machine learning.
Ability to connect data and AI work to real business impact.
Strong communication and stakeholder management skills.
Fluent verbal and written communication skills in both Mandarin Chinese and English.

About Apple

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10,000+ employees
Industry: Technology: Manufacturing

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