Founding Full Stack Engineer ($150k–$180k + Equity) at Tryrama.com at Jack & Jill/External ATS

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

07 Jun, 26

Salary

180000.0

Posted On

09 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Full Stack Engineering, Node.js, Python, Supabase, AI Model Iteration, Frontend Deployment, Architecture Design, System Thinking, Data Loops, Prompt Engineering, End-to-End Ownership, Scalability

Industry

Staffing and Recruiting

Description

This is a job that Jill, our AI Recruiter, is recruiting for on behalf of one of our customers. She will pick the best candidates from Jack's network. The next step is to speak to Jack. Founding Full Stack Engineer Salary: $150k–$180k + Equity Company Description: VC-backed AI supply chain startup Job Description: As the first non-founder hire, you will build an AI-driven workflow platform from the ground up to revolutionize global hardware development. You will own product end-to-end, from early architecture and model iteration to frontend deployment. Working in-person in San Francisco, you will ship daily and shape the technical foundation of a fast-growing company. Location: San Francisco, USA Why this role is remarkable: Join as the first non-founder hire, offering maximum influence over technical architecture and company culture from day zero. Join a well-funded team backed by top-tier VCs that has already secured six figures in revenue within its first three months. Work in a high-intensity, in-person environment focused on excellence over headcount, shipping real value to global users daily. What you will do: Design and ship end-to-end product features across the entire stack including Node.js, Python, and Supabase. Collaborate directly with founders to translate ambiguous customer needs into high-quality, scalable technical outcomes. Improve AI model quality through data loops, evals, and prompt engineering to handle complex, real-world operational data. The ideal candidate: Proven track record of shipping and owning production software end-to-end with high autonomy in fast-paced environments. Strong systems thinker comfortable with relational data and using AI coding tools to accelerate development speed. Excited by a high-commitment, in-office culture (6 days/week) and the challenge of solving messy, real-world data problems. Who are Jack & Jill? Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support. And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate. Next steps Step 1. Visit our website. Step 2. Click 'Talk to Jack'. Step 3. Talk to Jack so he can understand your experience and ambitions. Step 4. Jack will make sure Jill (the AI agent working for the company) considers you for this role. Step 5. If Jill thinks you're a great fit and her client wants to meet you, they will make the introduction. Step 6. If not, Jack will find you excellent alternatives. All for free. We never post fake jobs This isn't a trick. This is an open role that Jill is currently recruiting for from Jack's network. Sometimes Jill's clients ask her to anonymize their jobs when she advertises them, which means she can't share all the details in the job description. We appreciate this can make them look a bit suspect, but there isn't much we can do about it. Give Jack a spin! You could land this role. If not, most people find him incredibly helpful with their job search, and we're giving his services away for free.

Responsibilities

The engineer will be responsible for building an AI-driven workflow platform from the ground up, owning the product end-to-end from architecture to deployment. This includes shipping end-to-end features across the stack and improving AI model quality through data loops and prompt engineering.