Recruitment APP

Solution

1. Streamlined Job Posting: Faster, Smarter, and More Efficient

1.1. Single-Page Form vs. Stepper-Style Flow

Why I chose single-page over stepper-style:
A single-page layout allows experienced users like Kevin, an HR manager dealing with high-volume recruiting, to complete job postings more efficiently without the friction of multiple page transitions. At the same time, clearly divided sections within the page make it intuitive and non-intimidating for first-time or non-tech-savvy users like May, who may only post occasionally and prefer simplicity over complexity.This hybrid approach balances speed and clarity, accommodating both frequent power users and occasional task-doers.A single-page layout allows experienced users to complete the form more quickly without page transitions. At the same time, clearly divided sections make it intuitive for first-time users to follow without getting overwhelmed.

‍Collaboration with PM/engineers or constraints:
After discussing options with the PM and engineering team, we determined that a step-by-step flow would introduce additional complexity in state management and navigation logic, potentially delaying the project timeline. From a tech perspective, the single-page format was more maintainable and scalable—especially important given May's need for quick access and Kevin's demand for operational stability.

1.2. Supporting Both AI and Manual Flows (Hybrid Approach)

Why I supported both AI and manual workflows:

• Experienced HR professionals like Kevin, who often work with multiple job openings and prefer quick, efficient workflows such as pasting from existing templates or HR documents.
• Small business owners like May, who is a non-tech-savvy person, she may not trust or want to learn a new way to post the job.

By supporting both AI-assisted and manual flows, we respected users’ preferred working styles rather than forcing a one-size-fits-all experience. This flexibility allowed users to move fast or get personal—depending on their comfort and goals.

‍Team discussion and trade-offs:
During early discussions, PMs expressed concern that maintaining dual flows might increase onboarding effort and long-term design complexity. To address this, I proposed a progressive disclosure strategy: users first see an AI-generated suggestion, with the option to seamlessly switch to manual editing or start from scratch.

This approach kept the UI clean and beginner-friendly for May, while empowering power users like Kevin to stay efficient without additional friction.

‍Validation:
Although we haven’t conducted formal testing yet, Similar products like Workable and LinkedIn Recruiter offer AI-powered recommendations combined with manual adjustment options for resume screening. This hybrid design pattern has been proven to improve efficiency while preserving the flexibility and trust associated with human review.
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Moreover, according to Nielsen Norman Group’s usability heuristics, progressive disclosure helps reduce cognitive load, preventing new users from being overwhelmed by complex options.It aligns with the best practice of “showing essential information first while hiding advanced options”, which enhances task flow and improves the learnability of the interface.

1.3. Using a Shallow Step-by-Step Progress Bar to Encourage Adoption of the New Feature

Why I added a progress bar:
When we first introduced the new AI-assisted job posting feature, I anticipated that some users might feel uncertain or hesitant to try it—especially if they were unfamiliar with AI-driven tools.
To reduce that hesitation and guide them into trying the new experience, I added a three-step progress bar at the top of the form.By clearly showing that the process only involves three simple steps, the UI helps users feel that using this new feature is not complicated or overwhelming. This visual cue builds confidence and gently nudges users to proceed with the AI-assisted flow.

‍Engineering/UX constraints:
From a development standpoint, the progress bar was lightweight to implement and did not interfere with the overall layout. I designed it to be compact and visually aligned with the page structure to avoid clutter.

Decision and rationale (no testing yet):
‍Though we haven’t conducted user testing yet, research from UX experts like the Nielsen Norman Group suggests that visible progress indicators reduce uncertainty, increase perceived control, and improve task completion rates—especially for new or unfamiliar flows.

2. Improve the search experience

2.1.Smart Search & Efficient Filtering Design

Trade-off Analysis — Search Input & Filtering Experience

In this redesign of the job search interface, our primary goal was to improve search efficiency and reduce user friction. To achieve this, we implemented three key features: an intuitive input experience, intelligent keyword matching, and horizontal filtering & categorization design. Although we have not conducted formal user testing yet, our design decisions were based on common UX patterns, team discussions, and technical constraints. Below is an analysis of the key trade-offs involved:

2.1.1. Intuitive Input Experience

To streamline the resume screening process, we introduced real-time keyword suggestions and chip-based filters in place of traditional dropdowns or advanced filter panels. This allows users to input and adjust search conditions more fluidly and efficiently.

• For Kevin, who often needs to scan through dozens of resumes under time pressure, this lightweight interaction model reduces friction, enabling him to quickly apply multiple criteria without navigating through cluttered UI.
• For May, who may not use the system frequently, the familiar “search bar + suggestions” approach mimics platforms she already knows (like Shopee or 104 Job Bank), making onboarding smoother and more intuitive.

This hybrid pattern of suggestions + chips offers the best of both worlds: easy entry for less tech-savvy users and flexibility for power users.

2.1.2. Intelligent Keyword Matching

We implemented fuzzy search and keyword ranking (based on popularity and past queries) to improve search accuracy—even when users make typos or input vague terms.

• For Kevin, who often conducts bulk resume screening under time constraints, this feature reduces frustration when keyword inputs aren't perfect. He needs results fast, and error-tolerant matching helps avoid dead ends that slow down his process.
• For May, who may be unsure of job title terminology or makes occasional typos (e.g., typing “assistant” instead of “assistant”), fuzzy search ensures she still finds meaningful results, keeping her confidence high and reducing the need to “learn the system.”

This approach reflects a compassionate design mindset—meeting users where they are, rather than punishing them for mistakes.

Trade-off:
Compared to exact matching, fuzzy search introduced more backend complexity, requiring tuning of matching logic and query performance. PMs also raised concerns that early-stage data sparsity could affect the accuracy of keyword suggestions.

However, we determined that supporting imperfect input was more important, especially for users like May, who may lack confidence in technical systems, and Kevin, who needs speed and fluidity under pressure.

Validation:
This design is inspired by the error-tolerant search behavior seen in platforms like Google and major e-commerce sites. It also aligns with Nielsen’s usability heuristic of “error tolerance.”
According to Baymard Institute, up to 34% of users fail to get results without fuzzy search. Although this approach added technical complexity, it significantly improved search success and user experience.
Our priority was to reduce failed searches while improving recommendation relevance. In future iterations, we’ll refine ranking logic based on available data.
Key metrics for validation will include search success rate, error tolerance, and search engagement.

2.1.3. Horizontal Filtering & Categorization Design

We introduced a horizontally scrollable filter chip row that allows users to quickly swipe through and apply multiple filter criteria without opening dropdowns or switching views. This design helps maintain a clean interface while enabling precise filtering.

• For Kevin, who frequently screens large volumes of resumes and needs to compare multiple criteria at once, this format allows for fast multi-criteria refinement without extra clicks or modal interruptions—keeping his flow uninterrupted.
• For May, who prefers a simplified experience and often browses on mobile, horizontal chips provide a familiar, swipeable interface similar to e-commerce or travel platforms she already uses. It prevents the screen from feeling cluttered or overwhelming.

This interaction model balances efficiency for advanced users and approachability for casual users.

Trade-off:
To avoid overwhelming users with dropdown-heavy vertical layouts, we chose horizontal chips to save vertical space and reduce visual load—especially important on mobile, where screen real estate is limited.

While this introduced challenges in backend query optimization and scroll behavior on small screens, the engineering team tuned performance accordingly. PMs emphasized that we maintain filtering flexibility without degrading response time, which was particularly important for Kevin’s fast-paced workflow and May’s low tolerance for lag.

Validation:
This chip-based filtering system aligns with familiar UX patterns from Google Jobs, Shopee, and Airbnb, where users can quickly refine results with minimal friction. These models have proven especially effective in high-volume browsing tasks, which mirror Kevin’s needs, while also staying visually lightweight, ideal for May’s mobile-first use.

We plan to measure impact through:

• Filter interaction rate
• Resume click-throughs per filtered session
• Task completion time across device types

2.2. Enhancing Advanced Search: Real-Time Feedback, Mobile Optimization, and Improved User Experience

Trade-off Analysis — Filter Interaction Optimization＆Resume Matching System

2.2.1. Real-Time Filtered Results Count

Why we chose real-time result count instead of a traditional search button:
We implemented a real-time filtered results count that updates dynamically with each new filter, eliminating the need to hit a search button and wait for feedback.

• For Kevin, who often toggles between multiple filters during resume screening, this immediate feedback lets him quickly adjust search criteria and avoid wasting time on dead-end queries.
• For May, who may not be confident in using digital filters, the live result count acts as a gentle visual guide, reassuring her that each filter she applies is “doing something”—and preventing confusion when results drop to zero.

By showing feedback early, we helped users feel in control and less uncertain, improving task flow and reducing frustration for both casual and power users.

2.2.2. Resume Matching System

We introduced AI-powered match scores with visual indicators to help HR teams instantly identify high-potential candidates without opening each resume. This system supports faster decision-making and improves transparency in hiring.

• For Kevin, who reviews large numbers of applicants under time pressure, this solution enables him to prioritize top-matching candidates quickly while maintaining consistency across job roles and teams.
• For May, who may lack formal HR training and finds resume screening overwhelming, the visual tags give her clarity on why a candidate is a match (e.g., experience, location, salary)—offering confidence and guidance without needing to interpret dense resume content.

This feature bridges the gap between automation and human trust, helping both personas make decisions more efficiently and with greater clarity.

Why this design over traditional manual screening?

• Manual review is costly and inefficient: According to LinkedIn Talent Trends, HR professionals spend just 6–10 seconds per resume during the initial scan. For Kevin, this means a high risk of oversight. For May, this means she may miss relevant candidates due to limited experience or fatigue.
• AI + visual tags allow instant prioritization and structured evaluation, helping both users focus on what matters most without feeling lost or overwhelmed.
• This approach builds on proven models from platforms like LinkedIn Recruiter, Workable, and HireVue, validating its effectiveness and familiarity for users with previous exposure to modern hiring tools.

PM/Engineering Discussions & Technical Constraints:

To support PM goals of improving speed while preserving clarity, we implemented real-time visual tags to surface key info such as salary fit or experience level. On mobile, we adopted horizontal scroll chip design to handle information density in a way that’s easy to scan for both power users like Kevin and mobile users like May.

Why horizontal scroll instead of vertical stacking?

• For Kevin, who needs to quickly compare candidates, horizontal chips keep card layouts clean and enable side-by-side mental scanning.
• For May, who often uses mobile and prefers visually simple layouts, this design reduces visual clutter while still offering enough detail to make confident decisions.

It also aligns with Nielsen’s usability principles: reducing memory load, providing immediate visual feedback, and keeping key data visible without overwhelming the screen.

Validation:

This design builds on successful patterns from LinkedIn Recruiter, Workable, and HireVue, all of which use AI + visual scoring systems to improve speed, fairness, and hiring confidence.In our case, the system was intentionally adapted to support both:

• Scalability and precision for Kevin
• Transparency and guidance for May

Future plan: Validation metrics will include:

• Resume open rate
• Average screening time per candidate
• Ratio of high-match candidates reaching interviews
• HR feedback on trust and ease of decision-making

2.3. Enhancing HR Efficiency:  Smart Filtering System

Trade-off Analysis — Resume Filtering

2.3.1. Quick Filtering Mechanism

To support high-volume, time-sensitive resume screening, we implemented a quick filtering system that includes:

• A “hide viewed” toggle to help HR focus only on unprocessed candidates
• Bottom sheet menus for sort options like match score or application date
• Persistent match score visibility across all sorting conditions

This allows users to navigate, compare, and shortlist candidates efficiently—without leaving the list view.

• For Kevin, who needs to scan dozens of resumes in a short time, these features reduce context-switching and allow focused, high-speed evaluation without losing track of progress.
• For May, who may only check resumes occasionally and prefers simplicity, this layout keeps the interface clean and non-intimidating, while still giving her enough control to filter based on what's most important to her (e.g., nearby candidates or clear matches).

Validation:

While we didn’t run formal heatmap testing, the interaction model follows UX best practices from Nielsen Norman Group and Baymard Institute, both of which recommend progressive disclosure to reduce cognitive load and enhance usability.We also benchmarked platforms like LinkedIn Recruiter and Greenhouse, which use similar mechanisms—bottom sheets for filter control and persistent visual relevance indicators—to support fast-paced, high-accuracy workflows.

Metrics to validate this design include:

• Usage rate of sorting options
• Click-through rate on quick filter toggles
• Ratio of shortlisted candidates per session

These metrics will help us assess whether the system supports Kevin’s need for speed and clarity, while also being accessible and empowering for May’s occasional, task-focused use.

3. Enhanced Navigation and Visual Hierarchy

Rationale:
‍Clear grouping allows users to process information faster and reduces cognitive overload.

• For Kevin, it helps him quickly scan job listings, resume statuses, and actions without hunting across the screen.
• For May, it lowers anxiety by making the interface feel structured and approachable, especially since she uses the tool less frequently.

Trade-off:
Using card-based layout slightly reduced information density and required more design effort to maintain balance across screen sizes.
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‍PM/Engineering Discussion:
PM agreed that grouping improves onboarding and task clarity. Engineers noted minor increase in layout complexity but confirmed it was manageable.

‍Final Decision:
Adopted card grouping to serve both fast-paced users like Kevin and casual users like May.

Validation:
This approach aligns with Nielsen Norman Group’s principle of "chunking" information into digestible units, which reduces cognitive load and improves scannability. In enterprise tools, sectioned layouts have been shown to improve task efficiency by helping users focus on one type of information at a time.
‍Market Reference:
Platforms like LinkedIn Recruiter and Workable use grouped layouts for candidate profiles, tasks, and messages—supporting rapid review and multitasking.

3.2. Bold Typography vs. Subtle Weight
Why Choose Option A Over Option B?
Chosen (A): Use bold, larger text for headers and key labels
‍Not Chosen (B): Use consistent small/lightweight text throughout

Rationale:
‍Bold headers act as visual anchors.

• Kevin can scan dozens of candidate cards efficiently, quickly locating job titles or stages.
• May finds the clear hierarchy reassuring, knowing where she is and what to do next.

Trade-off:
Overuse might make the interface feel heavy. We mitigated this by limiting bold text to top-level sections and primary actions.

‍PM/Engineering Discussion:
PM questioned visual balance; design team validated the visual weight with layout mockups. Engineers confirmed no technical blockers.

‍Final Decision:
Kept bold styling but restricted to task-critical elements.

Validation:
According to Baymard Institute and Google Material Design guidelines, using typographic hierarchy (bold headers, increased size, spacing) improves readability and helps users locate key information faster—especially on mobile or dense interfaces.
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Market Reference:
Products like Notion and Airtable use bold and size-differentiated labels to guide attention across complex layouts without overwhelming the user.

3.3. Horizontal Navigation vs. Vertical Tabs

Why Choose Option A Over Option B?
Chosen (A): Horizontal scrollable chip-based filters
‍Not Chosen (B): Traditional dropdown or vertical filter panels

Rationale:
‍Clear grouping allows users to process information faster and reduces cognitive overload.

• For Kevin, it helps him quickly scan job listings, resume statuses, and actions without hunting across the screen.
• For May, it lowers anxiety by making the interface feel structured and approachable, especially since she uses the tool less frequently.

Trade-off:
Using card-based layout slightly reduced information density and required more design effort to maintain balance across screen sizes.

‍PM/Engineering Discussion:
PM agreed that grouping improves onboarding and task clarity. Engineers noted minor increase in layout complexity but confirmed it was manageable.

‍Final Decision:
Adopted card grouping to serve both fast-paced users like Kevin and casual users like May.

Validation:
Baymard research shows that users are more likely to engage with filtering tools when they’re visible upfront (e.g., chip-based) compared to dropdowns that hide options. Visibility reduces decision friction and increases interaction.
‍Industry Patterns:
Platforms like Google Jobs, Shopee, and Airbnb use horizontally scrollable chip filters to maintain a clean layout while encouraging refinement—especially on mobile.

‍Additional Support:
This pattern follows Nielsen’s “recognition over recall” heuristic, making options easy to find without the mental burden of opening and remembering dropdown menus.

3.4.Minimalist Navigation & Flat IA vs. Feature-Dense Layout
Chosen (A): Minimalist interface with visible, high-priority search/filter actions
‍Not Chosen (B): Feature-rich layout with more controls and info visible by default

Rationale:
‍Reducing visual noise helps users focus on core tasks.

• May is less confident with digital tools and benefits from a decluttered UI that reduces decision paralysis.
• Kevin, though an expert user, still benefits from streamlined navigation that lets him move fast without distractions.

Trade-off:
Some advanced settings are less discoverable initially. We mitigated this using icons and progressive disclosure patterns for those who need more control.

‍PM/Engineering Discussion:
PM agreed that improving first-time usability and mobile clarity was critical for business adoption. Engineers confirmed icon-based toggles and sticky filter bars would work within performance limits.

‍Final Decision:
Prioritize visual clarity over all-options-visible approach.

‍Validation:
According to Nielsen Norman Group, minimalist UI design improves user confidence and reduces abandonment—especially among novice users. Baymard Institute also emphasizes that visible search and filter options lead to higher task success rates in product listing interfaces.

Strong Visual Hierarchy vs. Uniform Interaction Styling

Chosen (A): Visually emphasize primary actions (e.g., Search, Contact, Update)
‍Not Chosen (B): Style all UI actions equally with uniform button and label designs

Rationale:
‍Differentiating primary and secondary actions prevents user confusion and supports clearer task prioritization.

• Kevin can immediately identify core functions and execute actions quickly.
• May feels more confident knowing what’s “safe to click” and what’s just optional or informational.

Trade-off:
Requires stricter UI consistency rules and occasional developer handoff attention to detail (e.g., color tokens, button states). The payoff is higher usability and fewer misclicks.

‍PM/Engineering Discussion:
PM was concerned that too much visual contrast might distract users. After reviewing component mockups, we aligned on a color system with tiered emphasis to reinforce hierarchy without overwhelming the UI.

‍Final Decision:
Adopt subtle-but-clear contrast rules for primary vs. secondary functions.

‍Validation :
Material Design and Apple HIG both advocate for visual weight differentiation to guide user attention. Research from Baymard Institute also confirms that over-uniformity leads to higher user confusion, especially on mobile and in high-speed tasks.

4. Simplify the Workflow (Auto-Save Drafts for Job Posting & Profile Completion)

Trade-off Analysis — Autosave Functionality

Chosen (A): Autosave with subtle toast notifications
‍Not Chosen (B): Manual “Save” button required for progress persistence

‍Rationale:
Autosave eliminates the need for users to remember to save manually, thereby reducing friction and minimizing the risk of data loss.

• For Kevin, who works across multiple job postings and screens simultaneously, autosave ensures task continuity, allowing him to switch contexts without worrying about unsaved changes.
• For May, who may not be confident navigating digital systems, autosave removes anxiety around “Did I save this?” and helps her stay focused on one step at a time, without requiring additional actions.

This reduces cognitive load for both personas and improves overall confidence in using the system.

Trade-off:
‍While manual saving may feel familiar to some users, it introduces failure points—especially if they forget to save or close the browser unexpectedly. We considered providing both, but chose a cleaner, more modern user experience centered on autosave, supported by lightweight toast feedback.

PM / Engineering Discussions & Constraints:

• PM Feedback:
  Strongly supported autosave for long-form job posting, where user frustration from data loss can be high. Toast messages were preferred to avoid distraction while still providing reassurance.
• Engineering:
  ‍No technical blockers; the backend was already equipped to handle autosave draft state.
• Business Context:
  Reducing task abandonment and increasing trust in the platform were key goals—autosave directly supports both.

Final Decision:
‍Implemented autosave as default behavior with toast confirmations. Did not include manual save button to avoid confusion or split behavior patterns.

Validation:

While A/B testing is pending, this design is aligned with UX standards set by platforms like Google Docs, Notion, and Dropbox Paper.
According to Forrester and Baymard Institute, autosave improves user satisfaction and completion rate, especially for long-form flows—up to 45% higher retention in task completion contexts.

We will validate effectiveness by tracking:
‍Drop-off rates during form usage
‍Error reports related to lost data
‍Qualitative feedback on perceived reliability of the system