Freelance Fullstack Engineers in CaliforniaFreelance Fullstack Engineers in California
AI Campaign Assets + Agents for DTC Growth
$50k+
Earned
19x
Hired
5.0
Rating
209
Followers
AI Campaign Assets + Agents for DTC Growth
Fullstack Engineer | 14+ yrs Shipping React,Next.js,Node,AI
$50k+
Earned
5x
Hired
4.9
Rating
102
Followers
Fullstack Engineer | 14+ yrs Shipping React,Next.js,Node,AI
Cover image for Led delivery of a cloud-native
Led delivery of a cloud-native K-12 student wellness platform that helps counselors monitor wellbeing, surface at-risk students, and act early—balancing product velocity with FERPA/COPPA-aware design and multi-tenant isolation (API RBAC, PostgreSQL RLS, district-scoped analytics). → Owned technical direction and delivery rhythm across a TypeScript monorepo (pnpm/Turbo): backend API, web app, shared packages, DB migrations, CI/CD gates, and staged deploys to GCP (e.g. Cloud Run), aligning engineering work with roadmap and release risk. → Orchestrated an AI assistant for counselors on Google Vertex AI (Gemini): system prompts, safety settings for K-12, function-calling tool design, multi-round tool loops, and strict separation so UI widgets render from verified tool results—not model hallucinations—with tracing/feedback hooks (e.g. Langfuse) for quality and auditability. → Drove access-control and compliance posture for sensitive student data: role/permission middleware, tenant-scoped queries, and defense-in-depth database policies—documentation and evidence suitable for enterprise security reviews (e.g. SOC 2–style narratives). → Partnered on product and program management: phased feature rollout, PR/branch discipline, and clear ownership of scope vs. risk so pilot schools could onboard without compromising data boundaries. → Shaped integrations and platform boundaries: Firebase auth, Neon PostgreSQL + Drizzle ORM, real-time patterns, BigQuery analytics isolation, and operational practices (testing, lint, type-check) that keep the stack maintainable at scale. → Established a disciplined, AI-accelerated delivery model—not ad-hoc prompting: Cursor rules and Claude skills/agents for repeatable workflows; Husky + lint/type-check/test gates; GitHub Actions CI/CD; Cursor Cloud Agents for automated PR review; and daily monitoring of E2E, coverage, and critical issues—owning AI-orchestrated implementation while enforcing accuracy, consistency, and observability
0
287
Full-Stack AI/ML Software Engineer
$50k+
Earned
1x
Hired
5.0
Rating
68
Followers
Full-Stack AI/ML Software Engineer
Full-stack Engineer | Applied AI/ML
$25k+
Earned
2x
Hired
5.0
Rating
14
Followers
Full-stack Engineer | Applied AI/ML
Silicon Valley Staff Software Engineer
$10k+
Earned
1x
Hired
5.0
Rating
15
Followers
Silicon Valley Staff Software Engineer
Framer Designer & Full-Stack Developer
$25k+
Earned
9x
Hired
4.8
Rating
160
Followers
Framer Designer & Full-Stack Developer
Cover image for I got tired of two
I got tired of two things: the nightly "what should I watch?" paralysis across a dozen streaming apps, and rating systems where a single score doesn't really tell you enough about a film. So I built Quartile (quartilefilm.io (http://quartilefilm.io)), a film discovery and rating platform. The core idea: instead of one ambiguous score, every film is broken into the five elements that actually matter — Plot, Acting (Narration for docs), Cinematography, Novelty, and Ending — each scored on a 1–4 scale: Well Below Average, Below Average, Above Average, Well Above Average. There's deliberately no neutral "average" middle — it forces you to actually take a position on each category instead of lazily defaulting everything to the middle, which keeps the ratings honest. Those five combine into a consistent score out of 10, but you can also see what each category scored, which helps you understand why a film is rated what it is. Sometimes a movie is really good but the ending or the acting is terrible, so the overall score isn't that high — and without the Quartile system you'd have no way to know that, because a single number can't tell you. That's exactly why I built it: I wanted to know why a film was rated what it was. Essentially it's a formula and some real quality control for how a film gets rated. The coolest part is it lets you filter in really cool ways. You can filter by Top in any category — so Top Cinematography shows only films rated Well Above Average (4) there. Top Novelty is one of my favorites, and one of the most unique filters we offer. You can also stack these with genre — so Top Novelty in horror, or Top Cinematography in sci-fi — which is where it gets genuinely useful for finding something specific to your mood. On top of the rating system you can log films you've seen, build and share playlists, and follow other users to see what they're watching. The same way you follow friends with good taste in music to find new songs on Spotify, film is a matter of taste too — you can follow friends with good taste in certain genres to find movies you'll actually like. It's in beta right now. Would love feedback from this crowd and general thoughts. Of course I'd be stoked if you made a profile and started rating movies and building playlists too — I'm going to grandfather in the first active users and give them something really cool on their profile eventually to mark that they were one of the first Quartile users.
15
13
456
Senior Full-Stack Engineer | Web Development | AI Automation
$5k+
Earned
1x
Hired
5.0
Rating
18
Followers
Senior Full-Stack Engineer | Web Development | AI Automation