Building the future of hiring.

Designing and building consumer facing software products that allow job seekers to search and apply to jobs on Personalized Career Site (PCS):

Notable projects I worked on:

• Brand new Pre-onboarding product that bridges the gap between the time a candidate is hired and the candidate starts their employment by allowing recruiters, hiring managers, and candidates complete and share specific forms needed before employment start such as I-9 form, background check

• Multistage application workflow that allows candidates to submit additional information in later stages of the application journey

• A new referral workflow that requires referred candidates to submit job application.

Built consumer facing software products that allow job seekers to search and apply to jobs on Personalized Career Site (PCS):

• Enhanced job search for remote, hybrid and onsite jobs

• Adapted job search and candidate application flows for private (confidential) jobs

Developed fair and bias-conscious matching systems that provide unbiased recommendations to job seekers and recruiters on Eightfold Talent Platform:

• Led efforts to monitor and address bias concerns across various protected categories (e.g. race, gender) in matching systems to foster enhanced fairness and inclusivity.

• Innovated and streamlined recruiter processes for job descriptions, leveraging OpenAI’s completion models to increase recruiter efficiency and improve job description specificity and inclusivity.

• Expanded the platform's global effectiveness by improving matching system performance in non-English languages.

• Ensured uninterrupted, high-quality user experiences by maintaining system stability and performance of matching systems.

• Shared industry knowledge and best practices through educational blog posts on bias in AI technology systems, emphasizing the importance of transparency and responsible AI development.

Researched computer vision and machine learning technologies focused on vehicles at traffic intersections for an MCity project at University of Michigan funded by Denso under Research Scientist Brent Griffin:

• Designed a multi-camera vehicle tracking system made of Faster R-CNN, SORT and fully connected neural networks (PyTorch) that can estimate GPS position of vehicles observed by different traffic cameras with a median error of 0.15 meters.

• Built a Python tool for a virtual environment that generates randomly shaped vehicles following random trajectories and corresponding observations of these vehicles in multiple camera views to train and evaluate our multi-camera vehicle tracking system

• Developed a Genetic Algorithm-based method to estimate intrinsic and extrinsic parameters for camera models that visualize real-life cameras in our virtual environment given a camera image, pixel values of selected calibration points and their corresponding GPS coordinates