Hardware User Experience Designer Graduate - Edge AI & Spatial Computing

About the Team We are building the future of edge AI — where on-device intelligence meets lightweight, wearable hardware such as Mixed Reality headwear and modular peripherals. As AI shifts from the cloud to the device, our team is focused on solving the "last inch" of physical interaction: making local AI feel natural, responsive, and seamlessly connected to the user's environment. You will work alongside mechanical engineers, industrial designers, ML engineers, optical and sensor teams, and human-factors specialists to ship experiences where hardware form, system behavior, and AI capability function as one coherent product. We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. 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, and which escalate 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 Minimum Qualifications - Individuals who are completing or have recently completed a Master's degree in Human-Computer Interaction (HCI), Industrial Design, Interaction Design, User Experience (UX), Human Factors Engineering, Design Engineering, Computer Science (HCI/AR/VR focus), Mechanical Engineering (Product Design/Human Factors), Cognitive Science, or a 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. Preferred Qualifications - Experience designing state-driven spatial visualizers and ambient indicators that dynamically construct themselves from multi-agent states, geometric transformations, and real-time environmental conditions. - Familiarity integrating generative workflows directly into engineering tools via protocols such as the Model Context Protocol (MCP), rather than relying on faked interactions or legacy visual scripting. - Experience defining multimodal guardrails for gaze, gesture, and tactile interactions, including clear UX policies for autonomous action versus human override. - Background designing low-latency interrupt and latency-masking patterns for edge-driven agents, using ambient cues to keep users anchored during inference and handoffs. - Experience designing feedback loops for systems that balance objective physical constraints (structural integrity, thermal/computational limits) with subjective human factors (aesthetic preference, physical comfort), including AgentRL-style tuning workflows. - Experience shipping AI, AR, VR, MR, or wearable hardware products from concept through manufacturing.