Problems You Might Own

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.

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
• What you build creates reusable leverage for later work, including responsible and verified uses of AI

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