Job Description

Full-Stack Software Engineer (Agents & Rapid Prototyping)

Location: Remote (US-friendly hours)
Type: Full-time (founding/early engineer)
Team: Small, passionate, ego-free builders

Why this role

We’re hunting for product–market fit . That means we ship POCs fast, test hypotheses daily, and aren’t afraid to throw work away if the data says so. If you code for fun , ask “ why ” before “ how ,” and love turning messy problems into scrappy automation that actually saves people time—this is your playground.

What you’ll do

• Prototype at lightning speed: Build and ship POCs, internal tools, and production features in days—not weeks.

• Work with LLMs & SLMs: Integrate, evaluate, and fine-tune models; design agentic workflows that reduce manual work.

• Automate the boring stuff: Sniff out bottlenecks, redundancies, and paper cuts; replace them with lightweight automations.

• Balance build vs. buy: Pragmatically choose off-the-shelf services vs. custom code; optimize for speed, cost, and learning.

• Own the stack end-to-end: APIs, data models, frontends, deployments, instrumentation, and on-call for what you build.

• Collaborate without ego: Pair with design, product, and GTM to clarify the “why,” define thin slices, and ship continuously.

• Ruthlessly iterate: Be comfortable restarting weekly if needed; measure outcomes, not lines of code.

Outcomes (how we’ll measure success)

• 2–3 prod-quality launches/month from idea → shipped.

• Clear learning loops : each release produces measurable signal (conversion, retention, time-saved).

• Automation ROI: hours saved/team/week; decreased cycle time; reduced ops tickets.

• Agent impact: at least one agent in steady use by internal or pilot users within first 60 days.

You might be a fit if you

• Have 4+ years building full-stack products (or equivalent portfolio/hacker track record).

• Are fluent in a modern web stack (e.g., TypeScript/Node , Python , React/Next ; Postgres/Redis; REST/GraphQL).

• Have shipped LLM/SLM features (tool use, RAG, function calling, evals, latency/cost tuning).

• Can design simple, resilient backends; write clean, testable code; and deploy via CI/CD to a major cloud.

• Love debugging with data : logs, traces, metrics; you instrument before you guess.

• Communicate crisply, ask sharp questions, and default to action.

• Thrive in ambiguity; you don’t need a JIRA novella to start building.

Nice to have

• Vector DBs, embeddings, prompt/version management, offline evals.

• Agent frameworks (e.g., OpenAI tools, LangGraph, custom planners/executors).

• Queueing/event systems (e.g., Kafka, SQS), WebSockets, streaming.

• Security and privacy basics (authn/z, secrets, PII handling).

• Experience with low-cost scrappy stacks (Cloudflare, Fly.io, Supabase, Firebase).

Our stack (evolving)

• FE: React/Next.js, Tailwind, tRPC/GraphQL

• BE: Node/TypeScript and/or Python FastAPI

• Data: Postgres, Redis/Upstash, optional vector store

• Infra: Vercel/Fly.io/Cloudflare + Docker; CI/CD (GitHub Actions)

• AI: OpenAI, Anthropic, local SLMs when useful; evaluation harness + prompt/version control

How we work

• Learn > Perfect: Ship thin slices, get signal, iterate.

• Write it down: Short RFCs → prototype → user feedback within days.

• Builder culture: No rockstars, no heroes—just ownership and curiosity.

• Time-zone friendly: Async first; quick huddles when needed.

Interview flow

1. Intro chat (30 min): goals, mindset, what great looks like.

2. Builder exercise (home or live, 2–4 hrs): ship a tiny agent or automation; explain trade-offs.

3. Technical deep dive (60 min): architecture, data modeling, reliability, AI evals/cost.

4. Team fit (30 min): collaboration, feedback loops, “why before what.”

Compensation

Competitive salary + meaningful equity. We optimize for ownership and impact .

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