About Labrynth

Labrynth accelerates progress by streamlining regulatory complexity. We build AI-powered platforms that navigate complex regulations, generate audit-level documentation, and provide certainty, not shortcuts. Our technology serves clients across heavily regulated industries including energy, compliance, and government regulations.

We operate as a forward-deployed engineering organization: small, high-velocity teams embedded directly with clients to rapidly discover needs and ship production-quality solutions.

About the Role

We are hiring a Forward Deployed Engineer to work directly with customers on regulated, operational workflows and turn that field work into a production product.

FDEs sit close to the customer and close to the code. You will map real workflows, identify the first useful product slice, implement it, verify it, demo it truthfully, and help decide what should become a reusable platform. The work spans three modes:

Field: shadow operators, model decisions, find evidence sources, and understand where the workflow is slow, risky, or brittle.
Build: ship production slices across UI, API, data, permissions, tests, and deployment paths.
Productize: turn customer-specific learning into primitives, configuration, evals, playbooks, or roadmap changes.

What You'll Do

Run customer discovery, workflow shadowing, and field notes with operators and decision owners, until you can name the users, states, evidence sources, exceptions, and the real operating constraint
Implement thin product slices in a live codebase across frontend, backend, data, and integrations, where the happy path works, unsafe paths fail, and the behavior survives realistic data
Write tests, run realistic paths, inspect logs, and document what is proven, missing, or uncertain, so every customer demo is backed by evidence, not optimism
Explain tradeoffs, risks, and next steps to non-technical customers without overclaiming
Identify reusable patterns from field work and feed them into product and engineering, so the next customer gets faster onboarding, safer workflows, or more reusable product
Carry ambiguous work end to end: discovery, build, demo, rollout, and follow-up

What We're Looking For

We don't need you to have used every tool in our stack. We need someone who can move safely across this kind of system and learn the missing pieces quickly.

You have personally shipped production software and can explain what you touched, how you verified it, and what changed for users
You are comfortable in messy customer settings where the first request is rarely the real problem
You can talk to operators in plain language, then go back to the codebase and build the thing
Product UI: TypeScript, React, Next.js, shadcn/Tailwind; you can trace a user flow, change a screen, and respect server/client boundaries
Backend: Python (uv), Pydantic, Django/Django Ninja or FastAPI, background workers, and typed APIs
Data and auth: Postgres, migrations, service roles, tenant scoping, and auditability
AI systems: pydantic-ai agents, typed outputs, evals, and provider choice across Gemini, OpenAI, and Bedrock; you use AI tools for leverage but never treat generated output or a clean demo as proof
Cloud: Vercel, Cloudflare, AWS, GCP; you can debug across deployment, env config, logs, and customer-facing behavior
Evidence discipline: you naturally separate fact, inference, assumption, and risk
Product judgment: you resist one-off customization unless the lesson clearly belongs outside core product
Strong communication skills: you can explain what is safe, what is uncertain, and what happens next

Nice to Have

AWS experience (IAM, GitHub OIDC, Lambda, API Gateway, Secrets Manager, CloudWatch, least privilege);
Infrastructure as code experience
Experience in regulated industries (energy, compliance, government permitting, healthcare, finance)
Prior forward deployed, solutions, or founding engineer experience

What We Offer

High-impact work at the intersection of AI and critical infrastructure regulation
Direct customer exposure and a seat at the table when we decide what to build
Small team with outsized influence; your field learning shapes the product roadmap
Modern AI-native development environment (Claude Code, Cursor, multi-model orchestration)
Remote-first
Competitive compensation