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New York, New York, United States
Hybrid
We are building the first-ever bank for enterprise data. There are over 35 million businesses in the US alone, many of which are sitting on an asset they don’t know they have. At Replay, we help businesses monetize their historical operating data by licensing it to the frontier AI and data labs for model training. Replay sits at the intersection of advancing the frontier of AI, while helping companies generate millions in growth capital without having to give up equity, take on debt, or lay off employees.
We have scaled from $0 to a multi-eight-figure run rate in a matter of months
We have raised from top-tier investors, including Floodgate, Afore, Ludlow, and Hustle Fund
We are small enough that you will carry outsized responsibility and grow as quickly as the company does
You will partner with and build for some of the fastest and most important companies in the world
You will help build a massive, category-defining business from the ground floor
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.
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
The common thread across our product is turning difficult, failure-prone work into software people can understand and trust. These areas share one Full-Stack Product Engineer title and hiring bar, but they represent distinct work and ownership. You do not need to choose one when applying. We will discuss the most relevant current opening early, and the placement-specific portion of the interview will reflect that work.
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.
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.
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.
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
What you build creates reusable leverage for later work, including responsible and verified uses of AI
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
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
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
