Summary
Apple is where individual imaginations gather together, committing to the values that lead to great work. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. Here, you’ll do more than join something, you’ll add something.
Description
We are looking for a versatile Machine Learning Engineer / Next-Generation Data Scientist who can own the full lifecycle of intelligent solutions—from problem framing and data sourcing through to model development, deployment, and user-facing delivery.
With the acceleration of AI-assisted coding, we are seeking someone who can operate as a full-stack problem solver, leveraging machine learning, advanced analytics, and modern development tooling to rapidly build scalable, production-ready solutions.
You will work at the intersection of data, AI, and engineering—transforming complex operational and business challenges into impactful, deployable products. This role is ideal for someone who thrives in ambiguity, embraces new tools, and is motivated by delivering real-world outcomes rather than just models.
Responsibilities
Own end-to-end solution delivery: from problem definition through to deployed product
Engage with stakeholders to understand business problems and translate them into analytical solutions
Identify, source, and engineer relevant datasets (structured and unstructured)
Leverage modern AI tools (e.g. code generation, copilots) to accelerate development across the stack
Build lightweight front-end applications or interfaces to expose models and insights
Deploy solutions into production environments (cloud or on-prem) with scalability and reliability in mind
Ensure solutions are robust, reusable, and aligned with engineering best practices
Collaborate across data science, engineering, and operations teams to embed solutions into workflows
Stay current with emerging AI, ML, and tooling trends, and identify opportunities to leverage them
Minimum Qualifications
5+ years of experience in data science or machine learning, preferably within supply chain, operations, or a related domain.
Proficiency in programming (e.g. Python) and experience with modern ML frameworks
Experience using or willingness to adopt AI-assisted development tools (e.g. code generation, LLM-based tooling)
Ability to work across the full technology stack (data pipelines, modelling, APIs, basic front-end)
Understanding of software engineering principles (version control, testing, modular design)
Familiarity with cloud platforms (e.g. AWS) and deployment patterns
Strong problem-solving skills with the ability to operate in ambiguous environments
Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders
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