Edge AI Hardware UX Designer, Spatial Computing

ByteDance

San Jose (CA)

On-site

USD 120,000 - 180,000

Full time

10 hours ago

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Job summary

ByteDance is seeking a UX/AI-focused engineer for edge AI product development in San Jose. You will partner with customers, field teams, and engineers to translate real-world workflows into intuitive on-device AI experiences.

The role emphasizes rapid prototyping, multi-disciplinary collaboration, and designing state-aware UX across local and cloud architectures. You will work with ML and hardware teams to balance speed, privacy, and battery life, while crafting clear user controls and robust

Qualifications

Master's degree in HCI, Industrial Design, Interaction Design, UX, Human Factors Engineering, Design Engineering, Computer Science (HCI/AR/VR focus), Mechanical Engineering (Product Design/Human Factors), Cognitive Science, or related field.
Experience designing embedded systems, AR/MR hardware, wearable devices, or physical-digital products, demonstrated through academic projects, internships, research, or a portfolio showcasing 3D spatial and physical-form thinking.
Understanding of on-device AI and edge computing concepts, including Small Language Models (SLMs), local vector embeddings, and the tradeoffs between inference speed, battery life, thermal performance, and user experience.
Strong foundation in industrial design, ergonomics, and spatial interaction design, with the ability to consider how digital interfaces integrate with physical hardware constraints and human factors.
Experience collaborating across multidisciplinary teams (e.g., mechanical engineering, industrial design, embedded systems, or AI/ML) through coursework, research, internships, or project-based work to develop hardware-software experiences.
Proficiency in building interactive prototypes using modern UX and 3D prototyping tools (e.g., Figma, Three.js, WebGL, Unity, Unreal Engine, or similar), with familiarity analyzing sensor data (e.g., IMUs, spatial mapping) and an understanding of edge hardware constraints such as NPUs, thermal limits, and memory management.

Responsibilities

Partner directly with customers, field teams, engineers, and end users to understand real-world workflows, deployment conditions, and failure points, and translate them into clear UX hypotheses, interaction models, and prototype plans.
Design forward-deployed UX programs that adapt core product capabilities across industries, environments, and user groups without fragmenting the overall experience.
Define how on-device AI communicates system state, confidence, progress, uncertainty, limitations, and required user actions across Small Language Models, local multimodal models, vector embeddings, sensor fusion, and hybrid local-cloud architectures.
Determine which interactions stay local for speed, privacy, reliability, or safety, or which elevate to the cloud, and design graceful degradation under limited connectivity, compute, sensor quality, battery, or model performance.
Collaborate with ML engineers to understand model behavior and translate technical capabilities into predictable user-facing experiences, balancing inference speed, quality, thermal performance, battery, privacy, and responsiveness.
Design agentic interaction models — including intent interpretation, task delegation, confirmation, interruption, cancellation, correction, escalation, and recovery — with clear boundaries between autonomous behavior and explicit user control.
Design understandable representations of agent status (active, waiting, blocked, uncertain, completed, failed, requiring approval) and ensure users can inspect, redirect, pause, or reverse consequential AI actions.
Work with industrial designers, hardware engineers, optical engineers, sensor teams, and human-factors specialists to translate interaction requirements into physical product implications: sensor placement, button location, display behavior, haptics, weight distribution, visibility, and thermal comfort.
Design logic-based spatial systems that adapt dynamically to real-time data and hardware form factors, using ambient, haptic, auditory, and visual cues to communicate model state without overwhelming cognitive load.
Build high-fidelity interactive spatial prototypes and digital simulation frameworks to stress-test multi-agent behaviors against boundary conditions, hallucination spikes, and context-window failures before physical deployment.
Establish rapid feedback loops between field deployments and central product, design, research, and engineering teams, and identify where issues require software, model, hardware, workflow, or user-education changes