Role
Lead Product Designer
Team
1 Designer, 1 UX Engineer, Lead Engineer/CTO, CEO
Duration
2 months
Overview
Pixona was an early-stage AI recruiting co-pilot startup focused on helping recruiters and hiring managers automate workflows and improve the hiring process. My responsibilities were refining the product strategy, shaping an intuitive user experience, and building a scalable design system from the ground up.
Deliverables: End-to-end flows for AI Resume Ranking, an ATS/CRM shell to host recruiting tools, and design system with reusable components.
Problem
Recruiters and hiring managers struggle to efficiently screen candidates.
The traditional process of job screening is quite inefficient, where reviewing candidate applications is tedious for recruiters, especially without subject matter expertise for the specific role. Hiring managers, juggling other priorities, struggle to provide timely feedback, slowing the process of finding the right candidate.
Key pain points:
Hiring managers deal with misaligned expectations due to recruiters’ limited domain understanding.
Recruiters struggle with time-consuming application review due to large volume of applied candidates.
Candidates often experience delays or ghosting, which can harm the company’s brand reputation.
Solution & Impact
Solution — We’re introducing an AI-powered ranking tool to help hiring teams streamline the application review process.
Solution — We’re introducing an AI-powered ranking tool, embedded within an ATS/CRM system, to help hiring teams streamline the application review process.
This includes:
Ranks candidates based on tailored hiring criteria.
Provides suggestions and validation checks on prompting.
Offering SME-like suggestions/hints, which reduces reliance on hiring managers’ feedback
Natural language search, providing flexibility for candidate sourcing
This solution improves screening efficiency while enhancing the recruiter-hiring manager collaboration and candidate experience.
Impact & Expected Metrics
Although the product development was paused mid-way, our intended outcomes were to:
reduced screening times for recruiters and hiring managers
decreased back-and-forth communication between them
improve recruiter–hiring manager alignment with shared criteria
reduced candidate ghosting & enhanced candidate experience
Challenges & Learnings
The Good, The Hard, and The Learnings
It was super exciting to ideate AI-driven tools and experiment with new approaches while rethinking traditional recruiting workflows — and even reflecting on how it affects me as a candidate too (pretty meta 🤯).
Becoming a quick SME in recruiting/ATS workflows — from legacy systems to new AI entrants — while exploring AI’s role in sourcing, screening, documentation, and evaluation, all within weeks ⏱️
My biggest takeaway: building responsible AI means putting transparency and compliance in from the very start. Especially in recruiting, EEO safeguards aren’t “nice to have” - they’re essential.
Want to learn more about this case?
Explore the complete product design process my team used to develop a solution for efficiently screening high volumes of inbound applicants.
Role
Lead Product Designer
Team
1 Designer, 1 UX Engineer, Lead Engineer/CTO, CEO
Duration
2 months
Overview
Pixona was an early-stage AI recruiting co-pilot startup focused on helping recruiters and hiring managers automate workflows and improve the hiring process. My responsibilities were refining the product strategy, shaping an intuitive user experience, and building a scalable design system from the ground up.
Deliverables: End-to-end flows for AI Resume Ranking, an ATS/CRM shell to host recruiting tools, and design system with reusable components.
Problem
Recruiters and hiring managers struggle to efficiently screen candidates.
The traditional process of job screening is quite inefficient, where reviewing candidate applications is tedious for recruiters, especially without subject matter expertise for the specific role. Hiring managers, juggling other priorities, struggle to provide timely feedback, slowing the process of finding the right candidate.
Key pain points:
Hiring managers deal with misaligned expectations due to recruiters’ limited domain understanding.
Recruiters struggle with time-consuming application review due to large volume of applied candidates.
Candidates often experience delays or ghosting, which can harm the company’s brand reputation.
Solution & Impact
Solution — We’re introducing an AI-powered ranking tool to help hiring teams streamline the application review process.
This includes:
Ranks candidates based on tailored hiring criteria.
Provides suggestions and validation checks on prompting.
Offering SME-like suggestions/hints, which reduces reliance on hiring managers’ feedback
Natural language search, providing flexibility for candidate sourcing
This solution improves screening efficiency while enhancing the recruiter-hiring manager collaboration and candidate experience.
Impact & Expected Metrics
Although the product development was paused mid-way, our intended outcomes were to:
reduced screening times for recruiters and hiring managers
decreased back-and-forth communication between them
improve recruiter–hiring manager alignment with shared criteria
reduced candidate ghosting & enhanced candidate experience
Challenges & Learnings
The Good, The Hard, and The Learnings
It was super exciting to ideate AI-driven tools and experiment with new approaches while rethinking traditional recruiting workflows — and even reflecting on how it affects me as a candidate too (pretty meta 🤯).
Becoming a quick SME in recruiting/ATS workflows — from legacy systems to new AI entrants — while exploring AI’s role in sourcing, screening, documentation, and evaluation, all within weeks ⏱️
My biggest takeaway: building responsible AI means putting transparency and compliance in from the very start. Especially in recruiting, EEO safeguards aren’t “nice to have” - they’re essential.
Want to learn more about this case?
Explore the complete product design process my team used to develop a solution for efficiently screening high volumes of inbound applicants.
Role
Lead Product Designer
Team
1 Designer, 1 UX Engineer, Lead Engineer/CTO, CEO
Duration
2 months
Overview
Pixona was an early-stage AI recruiting co-pilot startup focused on helping recruiters and hiring managers automate workflows and improve the hiring process. My responsibilities were refining the product strategy, shaping an intuitive user experience, and building a scalable design system from the ground up.
Deliverables: End-to-end flows for AI Resume Ranking, an ATS/CRM shell to host recruiting tools, and design system with reusable components.
Problem
Recruiters and hiring managers struggle to efficiently screen candidates.
The traditional process of job screening is quite inefficient, where reviewing candidate applications is tedious for recruiters, especially without subject matter expertise for the specific role. Hiring managers, juggling other priorities, struggle to provide timely feedback, slowing the process of finding the right candidate.
Key pain points:
Hiring managers deal with misaligned expectations due to recruiters’ limited domain understanding.
Recruiters struggle with time-consuming application review due to large volume of applied candidates.
Candidates often experience delays or ghosting, which can harm the company’s brand reputation.
Solution & Impact
Solution — We’re introducing an AI-powered ranking tool to help hiring teams streamline the application review process.
This includes:
Ranks candidates based on tailored hiring criteria.
Provides suggestions and validation checks on prompting.
Offering SME-like suggestions/hints, which reduces reliance on hiring managers’ feedback
Natural language search, providing flexibility for candidate sourcing
This solution improves screening efficiency while enhancing the recruiter-hiring manager collaboration and candidate experience.
Impact & Expected Metrics
Although the product development was paused mid-way, our intended outcomes were to:
reduced screening times for recruiters and hiring managers
decreased back-and-forth communication between them
improve recruiter–hiring manager alignment with shared criteria
reduced candidate ghosting & enhanced candidate experience
Challenges & Learnings
The Good, The Hard, and The Learnings
It was super exciting to ideate AI-driven tools and experiment with new approaches while rethinking traditional recruiting workflows — and even reflecting on how it affects me as a candidate too (pretty meta 🤯).
Becoming a quick SME in recruiting/ATS workflows — from legacy systems to new AI entrants — while exploring AI’s role in sourcing, screening, documentation, and evaluation, all within weeks ⏱️
My biggest takeaway: building responsible AI means putting transparency and compliance in from the very start. Especially in recruiting, EEO safeguards aren’t “nice to have” - they’re essential.
Want to learn more about this case?
Explore the complete product design process my team used to develop a solution for efficiently screening high volumes of inbound applicants.