Summary
Problem:
Job seekers face chaos managing multiple applications, customizing documents, and feeling ghosted in the process.
Goal:
Design an AI-enhanced job tracking tool that streamlines workflows, supports personalization, and restores clarity.
Result:
A prototype of Robin App that combines job tracking, auto-assistive resume support, and personal insights.
Role: UX Designer
Tools: Figma, Miro, Google Forms
Skills: User research, Prototyping, Usability testing
Background
The Problem
Job seekers often struggle to stay organized and motivated during long job searches. With dozens of applications, deadlines, and resume versions to manage, the process becomes overwhelming, leading to missed opportunities and emotional burnout.
"I need to find jobs that actually match my skills, not random listings that don’t apply to me."
The Challenge
Design a tool that not only tracks job applications efficiently but also supports users emotionally and functionally.
"I want job applications to feel less overwhelming, like someone is guiding me through it."
Research
Competitive Analysis
Platforms like LinkedIn, Indeed, and Hired lack integrated application tracking, follow-up reminders, or emotionally supportive feedback mechanisms.
Key Takeaways of Competitive Analysis
Competitors like Huntr and Teal prioritize tracking over user motivation or emotional support.
Interviews
We conducted interviews with 16 job seekers across different employment stages, from recent graduates to those actively changing careers. The sessions were held in person and via chat/text, focused on uncovering their biggest pain points when managing job applications and exploring perceptions around AI in the job search process.
“After applying, it feels like I’m shouting into the void. No replies or feedback.”
Most platforms offer rigid, one-size-fits-all solutions that don't adapt to user goals.
Several tools present overloaded dashboards creating friction and overwhelming users.
Key Takeaways of Interviews:
Many struggled to tailor resumes and cover letters for each job due to time and volume.
Applicants often forgot to follow up on jobs, lacking reminders and centralized tools.
Interviewees valued acknowledgment, even rejection, over being ghosted.
Users saw AI’s potential but had concerns about data privacy and algorithmic bias.
Surveys
We surveyed 30+ job seekers to understand their pain points across the job application journey. Responses highlighted difficulties with tracking applications, writing tailored materials, and staying motivated, especially when receiving no feedback or responses.
91%
want reminders to follow up.
68%
want resume guidance tailored to job descriptions.
56%
said ghosting was the most frustrating part of job hunting.
Synthesis
After conducting 16 user interviews and reviewing survey data, our team synthesized findings by identifying recurring patterns in job seeker behavior, emotional struggles, and workflow inefficiencies. We focused on Jefferson’s experience, who is navigating the pressure of balancing work, parenthood and job applications.
Core Insights:
Users frequently forgot where they applied or when to follow up, leading to missed opportunities.
Tailoring resumes for each job felt repetitive, time-consuming, and mentally draining.
Participants described the process as isolating and demotivating, especially when receiving no feedback.
Users wanted a single platform to manage tasks, stay organized, and feel supported throughout the process.
Ideation
We began ideation by defining key user needs uncovered in interviews and surveys. From there, we developed user personas, mapped user flows, and prioritized core features such as smart job tracking, AI-generated resume insights, and timely follow-ups. These ideas were sketched, discussed, and refined collaboratively to align with both user expectations and technical feasibility.
Design opportunities
Simplify Task Management
Streamline AI Interaction Flows
Design a Flexible, Multi-Mode Interface
Explorations
During exploration, we tested variations of the Robin assistant’s interface, from chat-based guidance to dashboard-style navigation. Each iteration helped narrow in on a solution that felt intelligent, responsive, and genuinely supportive.
1. Personalized Features
Among the features explored after the ideation phase are learning, dynamic dashboard and quick access to curated podcasts or music to help users stay productive between tasks.
2. Easy tracking of job applications
Another idea was to make the app a hub that connects all the jobs applied across different platforms, and display their current status.
3. Dashboard & Planner
A centralized dashboard connects users to all tools, including a planner to manage job-related tasks alongside personal responsibilities.
Final Design
Robin’s final design brings structure, support, and motivation to the job search process. Every screen was crafted to reduce overwhelm, highlight relevant opportunities, and guide users through next steps with clarity and care. The result is an experience that feels both personal and empowering.
2. Daily Dashboard Interaction
The home dashboard surfaces today’s priorities, like interviews, follow-ups, and reminders, so users can stay organized and reduce decision fatigue during their job search.
1. Tailored Job Matches
Robin displays personalized job suggestions based on each user’s skills, preferences, and experience. Match percentages offer quick clarity, helping users focus on the most relevant opportunities first.
3. “While You Wait” Feature
To support users during moments of uncertainty, Robin recommends podcasts, music, and learning resources, turning passive waiting into productive time.
Insights
Outcomes
Robin AI reimagined the job search experience by combining user-centric design with practical AI features like job tracking, resume support, and skill gap identification. The project successfully addressed the frustrations of job seekers, especially around follow-ups, personalization, and emotional burnout, by creating a prototype that felt more like a personal assistant than a platform.
What I learned
Working on Robin AI taught me the importance of designing with transparency, empathy, and trust. I learned that job seekers want tools that feel personal and human, even when powered by AI. Data privacy, customization, and educational support emerged as non-negotiables for building user confidence.
What's next?
Next steps include refining the AI’s capabilities, integrating Robin across platforms, and conducting additional user testing to validate assumptions. We also plan to enhance diversity in training data and prioritize inclusive design as we move closer to a fully functional MVP.
Prototype