Staff Fullstack Engineer - Internal Tools

The role

You will build and run internal tools for the Applied AI benchmarking business. You will own architecture, data-pipeline design, and hands-on engineering for platforms used by delivery, contributor-ops, and engineering teams.

What you will do

• Partner with researchers and TPMs to translate requirements into technical designs
• Own internal workforce-management tools for AI delivery
• Own architecture and long-term technical direction across multiple services
• Extend IAA and audio-QA pipelines
• Design and ship new modules for the platform
• Own and extend the platform's API key provisioning and budget-governance system
• Build self-serve ChatOps-style automation
• Harden background worker and job-processing infrastructure
• Set and enforce testing, CI/CD, and deployment practices
• Raise the technical bar through code review, design docs, and mentoring

Required qualifications

• 5+ years of professional full-stack software engineering experience
• Track record of owning production systems end-to-end across more than one runtime/language
• Deep experience with a Python backend framework (FastAPI or comparable)
• Experience with async SQLAlchemy/Postgres
• Production experience in at least one statically-typed backend language (Go, Java, or similar)
• Strong React/TypeScript frontend experience
• Experience with component architecture, state management (Zustand, Redux, or similar), and a modern data-fetching layer (TanStack Query or comparable)
• Experience building and operating background job/worker systems
• Experience integrating with third-party and platform APIs including GitHub API, OAuth/OIDC SSO, and at least one LLM API
• Experience building Slack or comparable chat-platform bot integrations
• Comfort reading and extending applied-statistics or ML-adjacent code
• Solid grasp of CI/CD, containerized deployment (Docker, Helm, ArgoCD/GitOps or comparable), and infrastructure-as-code (Terraform or comparable)

Nice to have

• Experience with audio/speech pipelines: ASR (Whisper or similar) or audio-quality metrics (DNSMOS, librosa)
• Experience building internal tools for managing a data-labeling, annotation, or crowdsourced-contributor workforce
• Experience with inter-annotator agreement or statistical agreement metrics
• Notification and delivery systems experience
• Experience designing abstractions over heterogeneous data sources
• Experience building ChatOps-style automation
• Experience implementing short-lived, rotatable service-to-service JWT auth
• Comfort owning both sides of a system with genuinely different runtimes
• Prior experience as the primary or sole engineer on a small, high-leverage internal platform
• Experience with LLM-as-judge or LLM-based QA/review pipelines

About the company

LILT is a machine learning company founded in 2015 that uses AI, machine translation, and human-in-the-loop expertise to translate content. The company aims to make the world's information accessible to everyone regardless of language.