Projects

Problem

Talent.com is a job search site with listings in many countries, and many job seekers find it through Google's job listings. A job only appears there after the site sends it to Google's indexing service. In late June Google began rejecting every submission. Over six days the pipeline logged 15.9 million rejections, and in that first week the number of jobs Google had indexed fell from 1.6 million to 250,000. New jobs stopped reaching Google, expired jobs stayed listed, and the working theory inside the company was that the site was sending too fast. Underneath, the pipeline had silent failures of its own, and no alert covered any of it.

Solution

Tested the working theory against the data before anyone changed limits. Minute-by-minute logs showed the rejections began in an ordinary 1,093-call minute, after days of serving up to 73,092 calls a minute without error, and an hour later even a slow 415 calls a minute was rejected 100% of the time. That is the signature of a daily ceiling, not a speed limit, which pointed the fix at quota instead of throttling. To limit the damage while the fix was underway, built a dedicated job sitemap and submitted it through Google Search Console, giving Google a second way to discover jobs by crawling. It streams about 12 million eligible US jobs, and later seven more countries, into files of 10,000 links each and publishes the whole set at once, so Google never reads a half-written file. Along the way, fixed second-level sitemaps that had been broken for two years and added a missing database index that had made six of eight countries fail. Then fixed what the outage exposed inside the pipeline: a data replay that overloaded a shared cache and froze processing, almost a million jobs stuck because crashed runs never released them, a throttle set three orders of magnitude below the real ceiling, duplicate requests, and an eligibility rule tied to a flag Google had stopped updating. Redesigned how work is claimed so an interrupted run releases everything automatically, without a risky change to a 483 GB table. When the backlog later stopped shrinking, proved Kafka had quietly discarded unread data and that none of 222 existing alerts would have caught it, and showed that half of the jobs sent to Google are gone within three days, which explains why Google holds far fewer jobs than the site sends.

Outcome

During the outage successful submissions fell to a few hundred a day, no new jobs reached Google for 27 days, and expired job removals stopped from July 2 to July 27. Once the fixes and the quota change shipped, new job submissions resumed at 1.5 to 1.9 million a day, removals restarted to clear the backlog of expired listings that built up during the pause, jobs indexed in Google recovered from 250,000 to 950,000, and about 17% of active jobs became eligible for Google listings again. The job sitemap went live in eight countries, and its database lookup got 70x faster, from 83.8 ms to 1.194 ms. Since recovery the pipeline has peaked at 71,023 submissions in a minute with only 7 rejections across 49,624 active minutes. After the removal gate deployed, 0 of 49,927 sampled removals were duplicates. The research also produced a sizing proposal that halves the time to clear the removal backlog, from 36 days to 18, and a concrete ask for longer data retention and a lag alert. Each fix exposed the next design flaw in a system that had grown without monitoring or maintenance, so recovery became a sequence of measured repairs rather than one patch.

Tech Stack

Problem

The database table behind Google job listings had grown to about 600 GB. A job that runs every few minutes kept timing out after 30 seconds, so it never finished its work. At the same time, the database's automatic cleanup had stopped running, over 60% of the table was dead space, and a safety counter that eventually forces the database into read-only mode was climbing.

Solution

Read the database's own query plans in production and found a design problem, not a tuning problem. No index matched the query, and the table's statistics had never been collected with ANALYZE, so the database had nothing to plan with and scanned the entire table. Built the missing indexes and ran ANALYZE, but the database still misjudged how many rows matched (about 17.9 million when the truth was 3.28 million) and kept choosing the full scan. Adding an ORDER BY on the indexed column was what finally made it use the index at all, so added tests that fail if that line is ever removed. Measured which indexes were actually used and removed three that were not. Traced the stalled cleanup to a stuck replication process that was blocking the whole database server, and gave the infrastructure team the evidence and the order of fixes.

Outcome

The query went from 15.8 seconds to 87 milliseconds, 181 times faster, and the related removal query to 1.4 milliseconds. Once infrastructure cleared the stuck process, the backlog of retained change logs fell from 1030 GB to under 1 GB and the read-only safety counter dropped 89%. Removing unused indexes freed about 78 GB.

Tech Stack

Problem

Job sites receive the same job from many sources, such as employers, job boards, and recruiting platforms, and each copy can pay a different amount when someone clicks apply. Showing every copy clutters search results and can hurt how Google ranks the site. Showing only one risks sending the click to a source that pays less. A duplicate filter already existed for three countries, but nobody had checked whether it worked.

Solution

Measured the problem before proposing a fix. Analyzed a multi-million-job sample to find how many jobs were duplicated, how many duplicates came from different job boards, and how often the same job is re-imported. Found the existing filter had three separate bugs and had never removed a single job. Designed the fix around canonicalization: every duplicate points search engines to the oldest matching copy of the job as the original, so Google sees one listing instead of many. Wrote a decision document for SEO and marketing leaders, not engineers, that keeps the revenue question separate: which copy is shown is a search decision, and where the apply click goes is decided at the moment of the click, so it can still reach the best-paying source. That avoided a database redesign. Set a data quality bar for the new matching key before anything goes live.

Outcome

Established that 45% of jobs are duplicated and about 28% of pages could be consolidated into their oldest matching copy, with 59% of duplicate groups spanning different job boards. Leadership adopted the decision, the new matching key shipped, and every job imported since carries it at 100% coverage in each measured country. Canonicalization rolls out behind that data quality bar.

Tech Stack

Problem

The servers behind the main job search app kept using more memory and never gave it back. Because of that, the system that automatically adds and removes servers stayed pinned at its maximum, and pages served to Google had slowed from about 300 milliseconds to between 750 and 1,000. Everyone assumed a memory leak in the code, which usually means weeks of investigation.

Solution

Measured first. Each server container was allowed 14 GB, but Node.js read the memory of the whole underlying machine instead, so each of the four app processes in a container assumed it could use about 2 GB, more than the container could hold under the memory target the autoscaler uses. A memory map confirmed the growth was that built-in allowance filling up, not a leak. Set an explicit memory limit per process sized to fit the container, then ran a one-hour load test on a test environment before merging. Separately traced the slowdown to a switch to a cheaper ARM server type and gave the infrastructure team the evidence.

Outcome

During the load test the busiest process peaked at 82% of its new limit, no process crashed or restarted, and the container never ran out of memory. Response time held flat at the 95th percentile, only 0.024% of 58,392 requests failed, and 53% to 63% of memory was released within 15 minutes after the load stopped. The fix was merged after the test.

Tech Stack

Problem

The site told bots from people using a label each browser sends about itself, which is trivial to fake. Scrapers routed through thousands of home internet connections with a normal Chrome label were counted as real visitors. That quietly distorted traffic reports, regional numbers, and revenue forecasts, and wasted server capacity. In September a sudden flood of this traffic, mostly counted as human, took the site down.

Solution

Sampled 1,000 requests per page from the server logs and compared the suspicious pages with a normal job page. The suspicious pages came from 96% unique addresses using only 17 browser labels, the pattern of a rented proxy network, against 22% unique addresses on the normal page. Published corrections to earlier analysis that had used the inflated numbers. Put limits on the page inputs scrapers were cycling through, and improved bot detection. For the outage, rebuilt the timeline from logs, showed six networks caused about 47% of the surge while traffic flagged as bots actually fell, and measured the firewall signals the company already paid for, so the fix could start without buying a new security product.

Outcome

Proved one scraped page accounted for 22.5% of all traffic counted as human. Removing the scraped pages changed North America's share of real traffic from 30.9% to 42.0%, which changes where the business should invest. The outage review sized a 3.2x jump in hourly traffic, named its sources, and produced a block and rate-limit plan using existing tools.

Tech Stack

Problem

When a new version of the job search app went live, a large share of people clicking "apply" hit an error. The team was also chasing server errors Google reported on search pages, intermittent errors on the job page blamed on the search database, and a shutdown fix that did not seem to do anything.

Solution

Measured a release minute by minute and found about 18,800 failed requests in four minutes. The framework generated a new secret key on every build, so people still on the previous version sent requests the new servers no longer recognized. Made the key stable per environment, loaded the production key from AWS Secrets Manager in a way that keeps it out of the shipped software image, and made the build refuse to ship without it. Found that a setting meant to allow a clean shutdown was silently ignored, fixed it, and added an automated check that rejects unsupported settings. Sampled 100 of the job page errors and traced every one to the network layer, not the database. Traced every sampled Google-reported error to one place where user search text was used unsafely, and fixed two more places where user input could be injected into redirects and logs.

Outcome

The release before the fix lost 45.7% of apply clicks; at the next release clicks held flat. In repeated builds of the same code, stable request keys went from 0 of 182 to 182 of 182. All 23 Google-reported errors in a 1,000-page sample traced to one fix, verified by regression tests that fail 19 of 25 on the old code and pass 25 of 25 on the new. All 100 sampled job page errors had one cause, which cleared the search database as a suspect.

Tech Stack

Problem

AI coding assistants fail quietly in two ways. When no tool exists for a task, they improvise, so the same workaround gets rebuilt every time and never becomes reusable. When they get something wrong, the correction stays in one conversation and the next one repeats the mistake. Once AI agents can reach code, tickets, logs, and databases, that silent improvising is also a safety risk.

Solution

Built a feedback loop into the AI platform itself. Standing rules require the AI agent to say plainly when no existing skill covers a task, finish the job anyway, and name what should be built. The second time a task is done by hand, the agent asks whether it should become a reusable skill. Every mistake gets a note left exactly where the next person or agent will hit it, and a shared memory skill saves lessons to the team's repository instead of one person's laptop. Guardrails block the AI from writing to production databases, committing passwords or keys, or running tools that are not set up, and the skill index is generated automatically and checked so it never goes stale.

Outcome

Since June the platform took 230 changes and gained 17 new skills. It now includes 59 skills, 21 specialized AI agents, and 4 automated guardrails, with 24 tracked known issues and 18 changes made purely to record a lesson for next time. One cleanup moved 68 of 112 facts out of a single engineer's private AI memory into shared team documentation. Seven other engineers have contributed.

Tech Stack

Problem

Multiple backend services were independently calling the Google Geocoding API to resolve locations for jobs, search, and SEO. There was no shared cache or canonical location representation, so the same postal codes and cities were resolved over and over. Finance confirmed this was costing $5K per month in geocode spend alone, with growth expected as ingestion volume increased.

Solution

Authored and published the Location Service Consolidation PRD as the anchor doc for the 2026 SEO/GFJ priorities. Mapped every current consumer of location data, designed a single service with a shared cache and canonical schema, and sequenced the migration so each caller could adopt it independently. Partnered with Finance to lock in the cost baseline and the ROI story before engineering touched any code.

Outcome

$5K per month ($60K per year) in direct API spend identified as recoverable, confirmed by Finance. The PRD became the single source of truth for the consolidation effort and unblocked the engineering roadmap for the quarter. Removed a hidden dependency on an un-cached third-party on a critical path for a high-traffic site.

Tech Stack

Problem

next-auth had become a liability across four frontend services: security advisories accumulating, Microsoft Entra support requiring brittle patches, and middleware-based RBAC that was hard to reason about. Replacing it piecemeal would fracture the login experience; replacing it all at once would be an all-or-nothing cutover on production auth.

Solution

Built a dual-implementation behind an AUTH_IMPL flag so both next-auth and Better Auth ran side-by-side in a dedicated QA environment. Ported RBAC parity to Better Auth middlewares, switched the Microsoft provider to Entra-only via genericOAuth, and moved AZURE_AD_TENANT_ID validation from module load to runtime so builds stopped failing when the secret rotated. Once parity was confirmed, collapsed the dispatcher across all four services and dropped next-auth from package.json.

Outcome

Cut over four frontends with a dual-impl flag that made rollback a one-line toggle instead of a deploy. Eliminated the next-auth dependency entirely, closed the outstanding advisories, and unified auth on a single modern library with better RBAC ergonomics. The dual-impl pattern became the template for the next round of high-risk library swaps.

Tech Stack

Problem

The jobseeker frontend had 59% test coverage with dozens of suites hidden behind .exclusions, a growing pile of stale tests skipped during prior migrations, and a local-vs-CI coverage mismatch that hid real gaps. New features were shipping without tests because the existing suite could not be trusted to catch regressions. A Next.js and React 19 upgrade was looming that would hit every mocked component in the repo.

Solution

Drove the coverage initiative in roughly 50 commits across a week. Revived 60+ suites across modals, job cards, SERP components, and provider wrappers. Wrote new branch-coverage tests for auth flows, A/B branches, and server actions. Fixed the Babel and Jest transform so React Testing Library actually rendered. Aligned local coverage reporting with CI via .exclusions pass-through so the numbers stopped lying. Co-located every new test next to its component.

Outcome

Coverage climbed from 59% to 90.92% with zero failing suites. Branch and function coverage both crossed the 80% CI threshold. The team could land the React 19 and next-intl v4 upgrade with real confidence instead of hope, and ~90 newly-reliable suites became the regression net for every subsequent change on a site serving 5-8M pageviews per day.

Tech Stack

Problem

The jobseeker frontend had no end-to-end tests and no integration tests at all. Unit tests checked components in isolation, but nothing exercised a real user journey, search to job detail to apply, across the actual rendered app. Critical flows like sign-in, the /view job page, and A/B-gated experiences could break in production without a single test failing first, on a site serving 5-8M page views per day. A major Next.js and React 19 upgrade was in flight, exactly the kind of change that breaks integration seams unit tests cannot see.

Solution

Stood up the first Playwright end-to-end suite for the app, covering the highest-value journeys: job search and SERP, the /view job detail page, sign-in and auth, and direct-apply flows. Built a regression harness with a fixed NUUID so Statsig experiment bucketing stayed deterministic across runs, making A/B-gated UI testable instead of flaky. Established selector conventions (text, role, and test-id rather than styled-components hashed class names) so tests survived styling changes. Wired the suite into CI so the journeys ran on every change, not just on demand.

Outcome

Took the frontend from zero integration coverage to a real safety net across its core conversion paths. The e2e suite caught regressions at the seams between routing, auth, and rendering that unit tests structurally could not, and made the staged framework upgrade safe to ship phase by phase. Deterministic Statsig bucketing turned previously untestable A/B branches into reliable, repeatable checks.

Tech Stack

Problem

The /view job detail page, the single most valuable organic landing page on jobseeker, was losing indexing signal to Google in two structural ways. The Google Indexing API was being called per-job in a loop, hitting quota and timing out, so submissions silently failed. And JobPosting JSON-LD was emitting for jobs that were technically available but not actually indexable, polluting the schema signal that Google relies on to surface jobs in Google for Jobs.

Solution

Batched Google Indexing API calls in the jobs-seo-index service and added a VirtualService timeout so a slow batch could not cascade into a full request failure. Dual-gated JobPosting JSON-LD on robots=index AND google_indexed=1, both sourced from the SEO Index service instead of the DB, so structured data only emitted for genuinely indexable jobs. Added a /v1/gfj/invalidate-jobs endpoint so takedowns actually cleared both SEO flags and dropped the job from the feed.

Outcome

Indexing API calls stopped timing out and started succeeding in batches. JSON-LD became a reliable proxy for "this job is actually indexable," which matters on a site where a 1% indexing shift is tens of thousands of landing pages per day. The invalidation endpoint closed the loop so expired jobs left the index instead of lingering as stale entries.

Tech Stack

Problem

Google Search Console was reporting a massive volume of soft-404s on talent.com, peaking at 39.5 million URLs, the bulk of them on /view job detail pages. A soft-404 is a page that returns HTTP 200 but Google judges as empty or missing, and at that scale it was actively suppressing indexation and organic traffic on the site’s most valuable landing page. An initial fix knocked the count down to 10-12 million, then it stalled and crept back up. The issue had persisted for 3+ months and was effectively un-debuggable: a URL Googlebot classified as soft-404 on its scheduled crawl returned full, healthy HTML when tested live in Search Console, so the team was troubleshooting blind.

Solution

Drove the technical investigation across a multi-ticket epic. Pushed for and got a scoped one-hour Googlebot-only log capture on /view, the minimum data needed to stop guessing, which revealed Googlebot was issuing POST requests to /view and the backend was answering with tiny 500-650 byte HTTP 200 bodies, an infra-measured ~14.5 million soft-404s per day. Traced a second class to React Server Component flight-data URLs (the _rsc query param) returning JSON instead of HTML, and fixed it by detecting bot crawlers on _rsc requests and 301-redirecting them to the canonical HTML, with an X-Robots-Tag: noindex safety net for unrecognized agents. The campaign also widened bot detection (case-insensitive matching, Google Inspection Tool, a catch-all for unidentified crawlers), suppressed the "No longer accepting applications" banner for search engines so expired-but-live jobs stopped reading as empty, defined correct 404/410 handling for expired jobs with a matching Google-for-Jobs delete call, and migrated the page onto canonical 18-char job IDs across canonical tags, redirects, and JSON-LD.

Outcome

Converted a three-month, un-debuggable indexation crisis into a sequence of root-caused, shipped fixes. The scoped-log push surfaced the POST tiny-response root cause the team had been unable to see, and the _rsc redirect plus bot-banner fixes removed two entire classes of false soft-404 on the highest-value organic page on a site serving 5-8M page views per day, where a 1% indexing shift is tens of thousands of landing pages.

Tech Stack

Problem

Code review at team scale was bottlenecked on a handful of senior engineers, and the depth of review varied with whoever picked it up. Security, accessibility, coverage, and architectural concerns were easy to miss when the reviewer was rushed. The team needed consistent, comprehensive review on every MR without slowing the merge cadence or creating more review load for the seniors.

Solution

Designed and built a shared Claude Code review pipeline. The /code-review skill fans out to specialist sub-agents (unit tests, lint, types, coverage, security, performance, simplification) in parallel, then consolidates findings by severity. Checked the entire .claude/ directory into the monorepo with a teammate README and tracked skill paths so any engineer gets the same review without local setup. Added a typed auto-memory system, routing rules, and fan-out chains so context persists across sessions without bloating the window. Same architecture also powers /diagnose-ci, which triages failing GitLab pipelines in one command.

Outcome

Turned a single command into a senior-level, multi-dimensional review that runs in minutes. Freed senior engineers from routine review load and raised the floor on every MR across the team. A CI hook now runs the same pipeline automatically on every MR.

Tech Stack

Problem

Day-to-day engineering meant context-switching across a dozen disconnected systems, each with its own CLI, auth, and quirks: GitLab for code and CI, Jira for tickets, Confluence for PRDs, Grafana/Loki/Prometheus for observability, Kafka for event pipelines, AWS and kubectl for infrastructure, Teams for comms, Statsig for experiments. Routine work, diagnosing a failing pipeline, drafting an MR, searching logs mid-incident, meant remembering the exact incantation for each tool, and that knowledge lived in individual engineers heads instead of anywhere reusable.

Solution

Designed and built a personal AI development platform on Claude Code, checked into the monorepo so it could be shared with the team. A routing layer maps plain-language intent to 30+ purpose-built skills, each wrapping one system end to end: create an MR with team defaults, diagnose CI, deploy to QA or a personal dev environment, search Jira/Confluence/Teams, query Grafana logs and Prometheus metrics, inspect Kafka topics, run Athena and Redshift, read Kubernetes state. Hardened it for sharing: per-user secrets stay local, a SessionStart hook reports which integrations are configured, and a PreToolUse gate blocks any skill whose config is missing and points the user at setup docs. Added a typed auto-memory system and on-touch context files so project conventions persist across sessions.

Outcome

Collapsed a dozen tool-specific workflows into one conversational interface where the routing layer picks the right specialist automatically. Tribal knowledge that used to live in chat threads, the exact glab flags, the JQL, the Loki query, became codified, versioned, and reviewable like any other code. The whole toolbox ships through normal MR review, so improvements compound for anyone who adopts it instead of dying in a single session.

Tech Stack

Problem

The jobseeker frontend, serving 5-8M page views per day, had drifted roughly three years behind across its dependency tree: Next.js multiple majors back, React a major behind, a next-intl v3 layer whose v4 migration was a breaking API change, an aging Nx workspace, and a long tail of transitive packages carrying security advisories and blocking each other. Attempting it all in one branch would have been weeks of merge hell with no way to de-risk, and a single regression on a critical organic-traffic site could cost hundreds of thousands of pages in indexing or conversion on launch day.

Solution

Broke the modernization into a six-phase staged rollout on a long-lived feature branch: Nx 18 → 22, React 18 → 19, next-intl v3 → v4, Next.js 14 → 15 → 16, plus the dependent packages each major dragged with it. Every phase merged dev into the feature branch, repaired test drift, and deployed to a dedicated QA environment before the next one started. Repaired roughly 30 test suites broken by the React 19 and next-intl v4 API changes, and caught prod-build type errors that dev mode had silently tolerated.

Outcome

Now live in production. Carried the full jobseeker frontend from three years behind to current, through React 19, next-intl v4, and Next.js 16, without a production regression. Each phase shipped independently through QA, so rollback blast radius stayed small. The same branch picked up RBAC parity, the Better Auth migration, and a coverage jump from 59% to 90.92% along the way.

Tech Stack

Problem

Legacy Python/Tornado/CoffeeScript platform with 45+ minute build times was blocking rapid iteration, making deployments risky, and limiting engineering velocity. The technical debt had accumulated over years, creating maintenance burden and slowing feature development.

Solution

Led complete migration to Next.js/TypeScript/SCSS stack using Claude Code for assisted refactoring. Incrementally decomposed monolithic services into microservices, established modern CI/CD pipelines, implemented comprehensive testing, and standardized development practices across teams.

Outcome

Build times dropped from 45+ minutes to 75 seconds, enabling multiple daily deployments. Development velocity increased significantly with modern tooling, type safety, and improved developer experience. Reduced production incidents and accelerated feature delivery.

Tech Stack

Problem

Over 24,000 articles from The Muse and FGB were difficult for users to discover through traditional search and navigation. Article content represented significant value but wasn't being surfaced effectively, limiting engagement and monetization opportunities. Users needed a more intuitive way to find relevant career advice.

Solution

Designed and led development of Maya, an AI-powered content discovery tool that uses natural language processing to surface relevant articles. Built with React.js and Next.js, integrating with article APIs to provide intelligent search results. Positioned platform for AI-first search future while maintaining performance standards.

Outcome

Increased article pageviews by 3,500 unique users per month. Boosted organic reach and created PR-driven traffic opportunities. Opened new top-of-funnel monetization paths by improving content discoverability. Demonstrated technical leadership in emerging AI technologies.

Tech Stack

Problem

Job seekers had to click into separate pages to view job details, then use back button to return to search results. This multi-tab workflow created friction, interrupted browsing flow, and reduced application conversion. Users lost context switching between search and detail views.

Solution

Revamped job search UX to two-pane layout allowing jobseekers to browse listings and view details in same tab. Implemented inline navigation that updates URL for SEO while maintaining smooth UX. Created new indexable URLs for each job view state while keeping user in browsing mode.

Outcome

50% increase in job applications per user by reducing navigation friction. Improved SEO through new indexable job detail URLs. Enhanced user experience by maintaining search context while exploring opportunities. Demonstrated impact of thoughtful UX on core conversion metrics.

Tech Stack

Problem

Google prioritizes sites with direct application capability in Google Jobs search results. The Muse needed to enable on-site applications without rebuilding forms for each partner's unique requirements. Each ATS had different fields, validation rules, and security requirements for handling PII.

Solution

Partnered with multiple third-party applicant tracking systems and built dynamic form generator consuming JSON schema definitions. Created system supporting custom fields, validation rules, display ordering, and PII security. Implemented multi-phase approach with technical documentation, partner coordination, and POC variants for each ATS integration.

Outcome

Clickthrough rate increased 15% and job applications increased 10% by reducing friction. Achieved Google Jobs prioritization improving organic discovery. Built scalable platform supporting unlimited ATS partners without custom development for each integration.

Tech Stack

Problem

Company profile pages built on legacy monolith (CoffeeScript, Python, Tornado) had poor performance scores hurting SEO rankings and user experience. Mobile Lighthouse scores: Performance 62, Accessibility 96, Best Practices 83, SEO 89. Slow load times reduced engagement and conversion for job seekers researching employers.

Solution

Replatformed Company Profile pages as server-side rendering microfrontend using Next.js, TypeScript, Docker, AWS ECS, and CircleCI. Replaced legacy stack with modern standards while maintaining feature parity. Focused optimization efforts on Core Web Vitals and accessibility compliance.

Outcome

Achieved all-green Lighthouse scores: Performance 91 (+29), Accessibility 100 (+4), Best Practices 92 (+9), SEO 96 (+7). Dramatically improved page load times and user experience. Enhanced SEO rankings leading to increased organic traffic to company profiles and job listings.

Tech Stack

Problem

The Muse wanted to expand revenue beyond job seeker platform by enabling partners to leverage job search technology. Required building scalable multi-tenant architecture that could handle custom branding, domains, and configuration while maintaining single codebase.

Solution

Architected and led development of white-label platform with tenant isolation, dynamic configuration, custom domain mapping, and brand theming. Built admin tooling for tenant provisioning and management. Designed data isolation strategy ensuring security and performance across tenants.

Outcome

Successfully launched B2B SaaS product opening new revenue stream. Platform enabled partners to launch branded job search sites within days instead of months. Architecture supports unlimited tenants with minimal overhead.

Tech Stack

Problem

Infinite scroll job search prevented Google from discovering and indexing deep job listings. Search engines could only crawl the initial page load, leaving thousands of job postings invisible to organic search traffic and costing significant potential revenue.

Solution

Redesigned search UX from infinite scroll to paginated results with proper SEO implementation (canonical URLs, rel=prev/next, XML sitemaps). Collaborated with Product/Design to maintain user experience while optimizing for crawlability. Implemented progressive enhancement ensuring functionality without JavaScript.

Outcome

Google began indexing entire job catalog. Organic search traffic increased dramatically as job listings became discoverable. Improved rankings for job-related queries and reduced dependency on paid acquisition.

Tech Stack

Problem

Job search experience felt dated and didn't align with The Muse's modern approach to job seeking. Technical debt in search page architecture limited ability to iterate quickly and add features. Poor Core Web Vitals hurt SEO rankings. User engagement metrics showed room for improvement in browsing and discovery.

Solution

Complete UX refresh redesigning search interface from ground up while rebuilding technical architecture. Migrated to Next.js, Koa, Storybook, TypeScript, CSS Modules, and Docker. Collaborated with Product and Design teams to reimagine job discovery experience. Focused on performance optimization achieving perfect Core Web Vitals scores.

Outcome

Increased job tile views 8.7%, tile views per unique visitor 20%, and job tile clicks 21.9%. Achieved exceptional Core Web Vitals: LCP 2.23s, FID 0.02s, CLS 0 (perfect). Improved SEO rankings through performance gains. Created scalable, maintainable platform for future search innovations.

Tech Stack

Problem

Existing ad partner provided limited formats and poor viewability. Ad revenue was plateauing and user experience suffered from intrusive placements. Needed better monetization without degrading Core Web Vitals or user experience.

Solution

Led evaluation and migration to new ad partner with modern formats (native, video, rich media). Redesigned ad integration with lazy loading, viewability optimization, and performance budgets. Implemented A/B testing framework to validate revenue impact and user experience metrics.

Outcome

Revenue increased 15% with better user experience. Improved ad viewability and CTR while maintaining excellent Core Web Vitals. Established framework for ongoing ad optimization experiments.

Tech Stack

Problem

Google Core Web Vitals scores in red/orange range hurt SEO rankings and conversion rates. LCP, FID, and CLS metrics failed Google's thresholds, directly impacting search visibility and user experience during critical job search moments.

Solution

Implemented comprehensive Web Vitals optimization: image optimization with next/image, font loading optimization, code splitting, lazy loading, server-side rendering improvements, and third-party script optimization. Established monitoring and performance budgets to prevent regression.

Outcome

Achieved 90%+ green Core Web Vitals scores across all pages. Improved SEO rankings, increased organic traffic, and higher conversion rates. Established performance culture with ongoing monitoring and optimization.

Tech Stack

Problem

Job listings and articles not appearing in Google rich results (job cards, article snippets). Search engines struggled to understand content structure, limiting visibility in competitive job search market.

Solution

Implemented comprehensive JSON-LD structured data across all content types (JobPosting, Article, Organization, BreadcrumbList). Worked with SEO team to optimize schema markup and validate in Search Console. Built automated testing to prevent schema regression.

Outcome

Organic SEO traffic increased 10x as content appeared in Google rich results. Job listings displayed with salary, location, and company in search. Articles featured in Top Stories and article carousels. Dramatically reduced dependency on paid acquisition.

Tech Stack

Problem

Legacy ad platform across Vogue, The New Yorker, Wired, GQ, Bon Appétit, and 25+ other brands had poor viewability (45%), slow render times, and inconsistent implementation. Every millisecond of latency impacted global revenue across 229M+ monthly users.

Solution

Architected and implemented unified ad platform serving all Condé Nast brands. Removed jQuery dependencies, optimized bundle size, implemented lazy loading and viewability tracking. Created shared UI tooling and plugin architecture. Standardized testing achieving 80%+ coverage.

Outcome

Ad viewability jumped from 45% to 85%, dramatically increasing revenue. Faster render times improved user experience and Core Web Vitals. Reduced integration defects and accelerated feature delivery across all brands.

Tech Stack

Problem

Slow page loads (5+ seconds) and excessive network requests (100+ per page) created poor user experience, hurt SEO rankings, and reduced ad revenue. Mobile users particularly impacted, with high bounce rates on slow connections.

Solution

Implemented comprehensive performance optimization: lazy loading, image optimization, critical CSS, code splitting, CDN optimization, and reduced third-party scripts. Built responsive mobile-first architecture using SASS (BEM) and modular JavaScript. Established performance budgets and monitoring.

Outcome

Page load time decreased 54% and network requests cut 53%. Improved engagement metrics, better Core Web Vitals, higher SEO rankings, and increased ad revenue. Mobile experience dramatically improved.

Tech Stack

Problem

Indiana foster care system relied on manual paperwork, spreadsheets, and email to manage 12K+ children and 3K+ foster households. Processing took months, data was inconsistent, and staff spent significant time on data entry instead of helping families.

Solution

Founded software firm and built comprehensive platform handling parent recruitment, child placement, training compliance, mentorship forums, and developmental evaluations. Replaced paper/spreadsheet workflows with automated systems. Implemented secure single sign-on and encrypted remote access for field staff.

Outcome

Cut statewide foster/adoption processing time by 50%+, accelerating placements for vulnerable children. Eliminated manual paperwork and data entry roles, saving $400K+ annually. Platform expanded across state lines and served as model for child welfare digital transformation.

Tech Stack