Systems Engineer - Input Systems

AppleSan Jose, CA

About The Position

Apple's vision products — our head-worn compute platforms (HMD and glasses) — are designed to become the fastest way to do serious, detailed work when paired with Mac, faster than a laptop alone. This role owns the systems architecture for fusing spatial inputs (hand, wrist, eye, and head tracking) with the keyboard, mouse, and trackpad connected to Mac, targeting a meaningful improvement in input efficiency without adding physical or cognitive fatigue. You will define how discrete input modalities combine into one continuous interaction model, and you will be the one who proves — with data — that people can type, point, select, and manipulate faster on our vision products than on any other platform, while feeling just as comfortable as they do today.

Requirements

  • Bachelor's degree in Electrical Engineering, Computer Science, Robotics, Human Factors/Human-Computer Interaction, or a related field
  • 6+ years of experience in systems engineering, HCI, or input-technology roles, ideally on shipped consumer hardware
  • Deep experience in systems engineering for human input devices (HID), sensor fusion, or human-computer interaction on shipping consumer hardware
  • Experience with sensor fusion algorithms and multi-modal input systems
  • Fluency in the trade-offs between input speed, precision, and physical/cognitive fatigue across keyboard, trackpad, mouse, and spatial (hand/eye/head) tracking modalities
  • Track record of defining and driving end-to-end system requirements across sensors, firmware, the OS input stack, and application layer
  • Comfortable owning ambiguous, cross-functional problems that span hardware, firmware, ML/perception, human factors, and macOS software integration
  • Excellent written and verbal communication skills, with the ability to turn a fuzzy product ambition into a testable spec with clear acceptance criteria

Nice To Haves

  • Master's degree in Electrical Engineering, Computer Science, Robotics, Human Factors/Human-Computer Interaction, or a related field
  • Experience designing and running quantitative human-performance studies (e.g. words-per-minute, Fitts's-law target acquisition, task completion time) and defensibly translating results into system/product requirements
  • Working knowledge of fatigue and comfort measurement methods (EMG, perceived exertion scales, longitudinal wear studies) — including evaluating physical and/or cognitive fatigue in wearable or prolonged-use hardware — and how to hold comfort constant while pushing performance
  • Experience building or integrating novel text-entry or pointing techniques, or shipping input features that combine wearable sensors with traditional peripherals
  • Publications, patents, or shipped features in multi-modal input, text entry, or spatial interaction

Responsibilities

  • Define the system-level architecture for fusing spatial input (hand, wrist, eye, head) with keyboard/mouse/trackpad signals into a single, low-latency interaction model
  • Set and validate an ambitious input-efficiency improvement target against Mac-only baselines, across representative productivity tasks (text entry, navigation, selection, precision pointing)
  • Own the specification for how our vision products preserve best-in-class high-rate text input — keyboard remains the backbone for speed; fusion must augment it, not compete with it
  • Build the measurement methodology and test harness for input efficiency (throughput, error rate, correction cost) and for fatigue/comfort (physical and cognitive), and treat the two as co-primary metrics — no speed win counts if it costs comfort
  • Decompose the fused-input experience into system requirements and drive them through sensor, firmware, input-stack, and macOS-integration teams
  • Partner closely with Sense & Constellation (sensor selection/config), Display Systems (latency budget), hand/eye tracking, human factors, and product design to land a coherent end-to-end spec
  • Run structured human-performance studies, own the resulting data, and be the team's authority on what's actually faster versus what only feels faster
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