Machine Learning Engineer, Intelligent Sensing Technology - Incubation

Our Camera Incubation team is a multi-disciplinary team responsible for looking down the road and prototyping new experiences, architectures and technologies. We collaborate with design and product teams to bring new features across the Apple product line. Join us as together we explore concept prototypes, helping shape what intelligent cameras can sense, understand, and do-and the experience they create for the people who use them.

We're looking for a creative ML Research Engineer to join our incubation team, where you'll work across the full stack, from model training and systems integration to rapid prototyping. While working on a diverse portfolio of exploratory projects, you'll bring deep practical knowledge of ML/AI architectures and multimodal sensing applied to physical spaces, paired with a design-centric approach to moving ideas from experiment to integrated system. The ideal candidate is energized by open questions, comfortable navigating ambiguity, quick to reorient when new data shifts the direction, and always able to clearly articulate the motivation, tradeoffs, and risks behind their approach.

BS and a minimum of 3 years relevant industry experience in machine learning or AI engineering

Familiarity with state-of-the-art architectures including transformers, reinforcement learning, and predictive inference

Strong coding skills across modern ML frameworks (e.g. PyTorch), with a practical approach to tooling that includes AI-assisted development as a natural part of the workflow

Proven experience building end-to-end ML pipelines, from data acquisition and preprocessing through training, evaluation, and deployment

Experience architecting and integrating sensing systems that fuse multimodal signals (e.g. vision, audio, IMU, LiDAR) with ML models running on mobile, wearable, or robotic platforms

MS or PhD with substantial applied research experience in a relevant area

Ability to clearly communicate technical tradeoffs, risks, and rationale to both technical and non-technical collaborators

Demonstrated comfort with ambiguity and a track record of adapting quickly when direction shifts

Experience working in a research, incubation, or early-stage exploratory environment

Familiarity with on-device or edge ML deployment and its associated constraints

Background in robotics, embedded systems, or real-time sensing pipelines

Knowledge of practical Bayesian reasoning and methods

Experience with physics-based machine learning - including physics-informed neural networks, simulation-to-real transfer, or learned physical models

Cross-disciplinary collaboration experience - hardware, software, design, and research