Full Stack Engineer (Backend)

ABOUT US

Scalestack is building the AI infrastructure for autonomous enterprise agents.

We've had AI systems running in production since 2022, powering agentic infrastructure for companies like Redis, MongoDB, Harvey, Typeform, and Astronomer.

Built in New York, Miami, and Buenos Aires. We are backed by $6M in funding and investors including Zapier Fund.

Our platform is built on a proprietary orchestration engine that runs 70+ integration modules processing millions of records monthly. Our agents are LLM-agnostic: same workflow, any model provider, no logic changes.

ROLE DETAILS

Type: Full-time
Location: Remote
Level: Mid-Senior
Reports to: Head of Engineering

THE ROLE

We build a platform where AI agents orchestrate complex workflows, enrich data at scale, and surface results to enterprise users in real time. Every part of that chain — from the API to the UI to the background job — needs to work reliably and be maintainable as the product grows. This engineer owns their slice of that chain completely.

We are looking for a Full Stack Engineer who will ship reliable, well-designed features across the full stack, end to end, with minimal hand-holding. You will not hand off to a backend team when it gets hard or wait for a frontend engineer to wire up the UI. You own it — from schema to UI to production. This is an IC role with real ownership scope.

You'll join one of two engineering teams: the Orchestrator team (focused on our core execution engine) or the AI Workflows team (building agent-powered automation for enterprise customers). The product team is lean — one PM, a CTO who owns architecture and engineering leadership, and a co-founder driving product vision. You'll have direct access to decision-makers and real influence over what gets built.

WHAT YOU’LL WORK ON

Full-Stack Feature Delivery

Design and ship full-stack features from schema to UI
Build and maintain REST APIs consumed by the frontend and external integrations
Build UI components and views, including complex data tables, workflow builders, and AI agent output surfaces
Work directly with product and design, flagging ambiguities and scope risks before work starts

Backend & Data Pipelines

Write background jobs and data pipelines that process records reliably at scale
Integrate with third-party APIs and data providers, handling rate limits, retries, and failure modes
Own schema design, query performance, and background job reliability
Diagnose and fix performance problems across services you own

Frontend Surfaces

Build data-heavy tables, workflow builders, AI agent output views, and real-time status UIs
Handle real-time data patterns: polling vs. websockets, stale state, optimistic updates
Work with the AI team to understand agent output formats and build reliable UI contracts around them

Production Ownership

Own your services in production: deploy, monitor, debug, and fix them
Write tests and documentation as part of shipping, not as an afterthought
Contribute to infrastructure improvements that affect your services: logging, monitoring, alerting
Participate in architecture and design discussions that shape how we build things

HOW WE WORK

We're adopting lightweight agile ceremonies — weekly grooming, structured sprints, automated KPI tracking. Engineers are expected to communicate proactively when blocked, not wait to be asked. When you're stuck technically, you go directly to the CTO. Follow-ups happen in public channels. We value people who own their work end to end: you deploy it, you monitor it, you fix it when it breaks.

WHAT WE’RE LOOKING FOR

Required

Strong backend fundamentals: can design a clean API from scratch (resources, verbs, error codes, pagination, auth)
Understands background job systems: queuing, retries, idempotency, failure modes
Can diagnose query performance problems and knows what indexes actually do
Can build a non-trivial React UI: state management, side effects, performance — not just copying components
Comfortable deploying to AWS and diagnosing basic infra-level failures
Track record of owning features in production end to end: deployed it, monitored it, fixed it when it broke

Preferred

Experience with async complexity: background jobs, retries, eventual consistency, streaming data
Has worked on a system with real scale constraints and can talk about what broke and how they fixed it
Curiosity about the AI agent layer, even without direct experience building AI-powered products
Prior work at a B2B SaaS company serving enterprise customers

TECHNOLOGIES YOU’LL WORK WITH

React / TypeScript · Python / FastAPI · MongoDB · Redis Streams · PostgreSQL · AWS (Lambda, ECS, SQS) · LangGraph · LLM APIs · Event Driven architecture

WHAT SUCCESS LOOKS LIKE

30 Days: Ship at least one full-stack feature end to end with minimal review overhead, understand the codebase well enough to navigate it without a guide
60 Days: Own 2+ features independently, identify at least one thing that is broken or fragile and propose a fix, write code the team does not have to clean up after
90 Days: Own a meaningful surface area of the product, handle at least one production issue on a service you own — diagnosed and resolved
6 Months: Product and design trust you to flag problems and deliver what was agreed, contribute to technical discussions and improve something beyond your immediate ticket
12 Months: Reliably own features from inception to production with no supervision, make at least one architectural decision that improves how the team builds things, help onboard or review work from newer engineers

COMPENSATION AND BENEFITS

Competitive salary benchmarked to your experience and location
Flexible and Unlimited PTO
Equipment reimbursement
Lots of opportunity to learn, grow and make an impact

OUR VALUES

Ownership & Commitment: We take full responsibility for our work, see it through, and hold ourselves accountable to the outcome.
Deliver Results: We focus on outcomes. We ship things that matter, on time and at the right quality.
Insist on the Highest Standards: We refuse to accept mediocrity in our work. We raise the bar constantly and push each other to do the same.
Learn and Be Curious: We ask why, challenge assumptions, and treat every mistake as a chance to get better. The best ideas can come from anywhere.
Collaboration: We win as a team. We share context freely, support each other, and make decisions that are good for the whole, not just our own area.

INTERVIEW PROCESS

We move quickly and tell you where you stand at every step

Screening call — 5 min. Get to know each other and check mutual fit.
Culture fit calls— 45 min. Assess alignment with working style and values (2 calls total, with an optional third if needed).
Technical / skills conversation — 45 min. Deep dive on your experience, past decisions, and how you think.
Take-home exercise (OPTIONAL) — 3–4 hours. A practical problem that reflects what you would actually be working on here.
Decision