Full-Stack Product Engineer

Doist; New York (NY); On-site; USD 130,000 - 190,000; Full time

Job summary

Sunset is hiring full-stack product engineers to own hard customer problems from the first product decision through reliable production systems. You will work across UX, frontend, backend, data, testing, observability, and iteration—not simply one layer of the stack.

We have opportunities across three connected product areas: acquiring internal enterprise work data from the systems where it lives, guiding companies through dissolution, and building the product layer around a de-identification

Qualifications

• Three+ years of professional software engineering experience.
• Strong full-stack engineering with product and UX judgment.
• Experience in a startup with broad ownership and evolving requirements.
• Ability to own ambiguous problems from discovery through production.

Responsibilities

• Own ambiguous customer or internal-team problems from discovery through measurable production outcomes
• Build coherent vertical slices across frontend, backend, workflow state, data, testing, observability, security, and release
• Make complicated processes clear without hiding exceptions, uncertainty, or recovery paths
• Diagnose difficult production behavior and remove recurring root causes
• Establish useful measurements and improve customer, team, quality, or reliability outcomes
• Create reusable product and engineering capabilities that make later work faster and safer
• Use AI deeply in development and where it improves the product, with explicit evaluation and verification
• Work directly with Product, Design, customers, domain experts, and other engineers

Skills

Full-stack engineering

UX judgment

Startup experience

Ambiguity ownership

Tools

TypeScript

React

Node.js

Job description

About Sunset

At its core, Sunset was founded to help founders. We started by supporting startups through shutting down, but we have since expanded into unlocking a new revenue stream for all types of businesses. In 2025, we had a unique insight: the data every company generates each day through collaboration, communication, and building is some of the most valuable training data in the world. Public and synthetic data can only get frontier models so far, so the next generation of model progress depends on real, proprietary data grounded in how actual businesses operate. We are a primary source of it, partnering directly with the frontier AI labs building what comes next.

Why Join Sunset Now

We have scaled from $0 to a multi-eight-figure run rate in a matter of monthsWe have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle FundWe are small enough that you will carry outsized responsibility and grow as quickly as the company doesYou will partner with and build for some of the fastest and most important companies in the worldYou will help build a massive, category-defining business from the ground floor

The Role

We're hiring full-stack product engineers to own hard customer problems from the first product decision through reliable production systems. You will work across UX, frontend, backend, data, testing, observability, and iteration—not simply one layer of the stack.We have opportunities across three connected product areas: acquiring internal enterprise work data from the systems where it lives, guiding companies through dissolution, and building the product layer around a de-identification pipeline that makes sensitive data safe and useful. You do not need prior experience in these domains. Each opening is tied to a real area of ownership, and we will be clear early in the process about which current opening appears most relevant to your experience.

What You’ll Do

• Own ambiguous customer or internal-team problems from discovery through measurable production outcomes
• Build coherent vertical slices across frontend, backend, workflow state, data, testing, observability, security, and release
• Make complicated processes clear without hiding exceptions, uncertainty, or recovery paths
• Diagnose difficult production behavior and remove recurring root causes
• Establish useful measurements and improve customer, team, quality, or reliability outcomes
• Create reusable product and engineering capabilities that make later work faster and safer
• Use AI deeply in development and where it improves the product, with explicit evaluation and verification
• Work directly with Product, Design, customers, domain experts, and other engineers

Problems You Might Own

Bring fragmented enterprise data into one trustworthy system

Customers need to bring internal work data out of many SaaS tools, APIs, files, and export processes. Build the product that takes them from “our data lives over there” to a successful, verified transfer. You might design provider-specific export journeys, model long-running transfer state, make failures and recovery understandable, or build contracts, fixtures, and acceptance tests that keep every new source from becoming a one‑off. This also means maintaining clear provenance and health across every handoff.

Help companies navigate dissolution from start to finish

Build the software that helps a company wind down its operations responsibly. The product spans onboarding, forms, documents, auctions, permissions, government obligations, operational closeout, and the exceptions that appear along the way. You might model durable workflow state, generate or parse documents, reconcile conflicting information, design a safe team intervention, or make a consequential next step clear to a customer. The challenge is keeping the whole journey understandable and recoverable when reality deviates from the expected path.

Make the de-identification pipeline understandable and trustworthy

Our pipeline turns sensitive enterprise data into de-identified datasets without losing useful structure and meaning. Build tools that show what ran, surface what was missed or changed incorrectly, and support delivery decisions. You might seed synthetic data with subtle failures, replay past defects, construct golden examples, combine deterministic checks with bounded model-based judges, or design investigation interfaces that reveal what no single metric can. The goal is evidence the team can interrogate and trust.

What Success Looks Like

You ship complete product improvements that customers and the team trust and use

The customer, team, quality, or reliability outcome you set out to improve moves meaningfully from its baseline

Complex system state becomes understandable, failures become recoverable, and recurring problems receive durable fixes

Manual effort, support burden, and repeated work decline as the product improves

You Might Thrive Here If

• You have at least three years of professional software engineering experience
• You are a strong full-stack engineer with excellent product and UX judgment
• You have worked in a startup and enjoy broad ownership, changing context, and building without fully specified requirements
• You have owned ambiguous problems from discovery through implementation, production operation, and iteration
• You can make complex workflows clear while reasoning carefully about state, data integrity, testing, security, observability, and recovery
• You use evidence to debug systems and measure whether your work improved the outcome
• You use modern AI engineering tools fluently, verify their output, and understand how to evaluate AI product behavior
• You communicate clearly across technical and customer-facing teams

This Role May Not Be for You If

• You want to work exclusively in one layer of the stack
• You need a clean handoff between product definition, design, and engineering before you begin
• You prefer predictable feature work over ambiguous product and systems problems
• You do not want AI tools to be part of your daily engineering workflow

Bonus

• Strong TypeScript, React, Node.js, or comparable full-stack experience
• Experience with integration-heavy SaaS, APIs, OAuth, uploads, files, or asynchronous workflows
• Experience with document workflows, internal tools, evaluation systems, data quality, or production diagnosis
• Experience building high-fidelity, resettable simulation or evaluation environments using synthetic data, seeded failure modes, multi-step state, and programmatic verifiers to test data or AI systems
• Experience with multi-tenant, compliance-sensitive, or otherwise high-trust products
• Experience shipping trustworthy AI-assisted workflows