NxT Level
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 Intro chat
(30 min): goals, mindset, what great looks like.
Builder exercise
(home or live, 2–4 hrs): ship a tiny agent or automation; explain trade-offs.
Technical deep dive
(60 min): architecture, data modeling, reliability, AI evals/cost.
Team fit
(30 min): collaboration, feedback loops, “why before what.”
Compensation Competitive salary + meaningful equity. We optimize for
ownership
and
impact .
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 Intro chat
(30 min): goals, mindset, what great looks like.
Builder exercise
(home or live, 2–4 hrs): ship a tiny agent or automation; explain trade-offs.
Technical deep dive
(60 min): architecture, data modeling, reliability, AI evals/cost.
Team fit
(30 min): collaboration, feedback loops, “why before what.”
Compensation Competitive salary + meaningful equity. We optimize for
ownership
and
impact .