Senior ML Engineer - On-Device AI & Spatial Computing

Apple; Sunnyvale (CA); On-site; USD 185,000 - 325,000; Full time

Job summary

Apple seeks a Senior Machine Learning Engineer to design and implement production‑level ML systems for spatial computing and AI. You will translate research into on‑device, production‑ready solutions that delight users.

You will collaborate with researchers to develop unified context representations and device‑agnostic frameworks, enabling intelligent experiences through real‑time and historical contextual data management.

Qualifications

• BS and 7+ years of experience in software engineering, computer vision, machine learning or related fields.
• Strong experience in Python.
• Foundational understanding of machine learning: Familiarity with ML algorithms and development pipelines, with the ability to work effectively with ML practitioners and integrate ML components into production systems.

Responsibilities

• Working with ML and system engineers and designers to develop quick proof-of-concepts with novel hardware for innovative applications.
• Implementing and optimizing algorithms to run efficiently on mobile devices.
• Building and optimizing efficient machine learning pipelines.
• Developing cutting-edge AI systems that push the boundaries of what is possible.
• Collaborating cross-functionally to understand algorithmic constraints and dependencies.
• Cooperating with your team members to prepare presentations, papers, and talks to explain your inventions.
• Advising on feature definitions/promising development directions in cross-functional groups.

Skills

Python

C++

Swift

Math

Communication

Collaboration

iOS/macOS development

Education

MS/PhD or 5+ years experience

BS and 7+ years experience

Job description

Apple seeks a Senior Machine Learning Engineer to design and implement production‑level ML systems for spatial computing and AI. You will translate research into on‑device, production‑ready solutions that delight users.

You will collaborate with researchers to develop unified context representations and device‑agnostic frameworks, enabling intelligent experiences through real‑time and historical contextual data management.