Staff Fullstack Engineer - Internal Tools

About LILT

AI is changing how the world communicates — and LILT is leading that transformation.

We're on a mission to make the world's information accessible to everyone , regardless of the language they speak. We use cutting-edge AI, machine translation, and human-in-the-loop expertise to translate content faster, more accurately, and more cost-effectively without compromising on brand, voice, or quality.

At LILT, we empower our teammates with leading tools, global collaboration, and growth opportunities to do their best work. Our company virtues— Work together, win together; Find a way or make one; Dance in the customer's shoes; Quicker than they expect; Quality is Job 1 —guide everything we do. We are trusted by Intel Corporation , Canva , the United States Department of Defense , the United States Air Force , ASICS , and hundreds of global Enterprises. Backed by Sequoia, Intel Capital, and Redpoint, we’re building a category-defining company in a $50B+ global translation market being redefined by AI.

As part of LILT’s Internal Tools group, you will build and run the tools that LILT's own delivery, contributor-ops, and engineering teams rely on to operate LILT's Applied AI (AAI) benchmarking business — which builds and delivers multilingual benchmarks and evaluation data to frontier AI labs using LILT's global network of subject-matter experts. As the AAI business grows, expect the scope of these platforms to grow in parallel. This platform sits directly on the critical path of paid customer deliverables with real SLAs, maintained by a small, high-leverage engineering team that produces reliable, production-ready tooling. You will own significant surface area across both end-to-end: architecture, data-pipeline design, and the hands-on engineering that keeps this infrastructure reliable. This is a high-impact, high-visibility role: the reliability and craft you bring directly enables on-time, high-quality delivery for some of the most prominent AI labs in the world.

In this role, you will drive the long-term technical strategy for internal platforms while working directly with the delivery, ops, and engineering teams across the business who depend on them daily. This team ships cross-service automation in careful stages — advisory first, then assistive, only later decision-relevant, always with a human fallback — and you'll be expected to hold that same bar as you extend these systems. You will partner with a small existing team to raise the engineering bar across two very different runtimes, set technical direction, and make the calls that determine how this infrastructure scales as the business grows.

What You'll Do

Partner with the benchmarking business's researchers and TPMs to translate new benchmark and data-quality requirements into scoped technical designs

Own the internal workforce-management tools on our internal platform for AI delivery for hundreds of external contributors: vetting flows, candidate assessment, QC, payment/delivery tracking, and roster/reporting exports

Own architecture and long-term technical direction across multiple services and the platform

Extend IAA and audio-QA pipelines: annotator outlier detection, ASR sidecar enhancements, LLM-based QC, and DNSMOS/librosa audio-quality scoring

Design and ship new modules on the platform that plug new benchmark and vetting workflows into the existing multi-stage review lifecycle

Own and extend the platform's API key provisioning and budget-governance system

Build self-serve ChatOps-style automation for the internal engineering org and contributor base to enable accelerated annotator workflows and query resolution

Harden background worker and job-processing infrastructure

Set and enforce testing, CI/CD, and deployment practices across both codebases — from unit/integration testing through infrastructure-as-code and release automation

Raise the technical bar for a small, high-leverage team through code review, design docs, and mentoring as the surface area grows

What We're Looking For

Required

5+ years of professional full-stack software engineering experience, with a 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) plus async SQLAlchemy/Postgres, alongside production experience in at least one statically-typed backend language (Go, Java, or similar)

Strong React/TypeScript frontend experience — 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 (queue-driven or polling-based) with failure tolerance and idempotency in mind

Experience integrating with third-party and platform APIs — including the GitHub API, OAuth/OIDC SSO, and at least one LLM API (Gemini, OpenAI, or similar) — handling auth, rate limits, and webhook-driven sync

Experience building Slack (or comparable chat-platform) bot integrations that automate internal workflows — resource provisioning, approvals, budget/TTL enforcement — with real operational guardrails, not just CRUD features

Comfort reading and extending applied-statistics or ML-adjacent code (agreement metrics, audio-quality scoring, or comparable data-quality tooling)

Solid grasp of CI/CD, containerized deployment (Docker, Helm, ArgoCD/GitOps or comparable), and infrastructure-as-code (Terraform or comparable)

Experience debugging and optimizing native-library (numpy/scipy/onnxruntime-class) memory growth in long-running Python worker processes via safe, boundary-aware process recycling — not just raising memory limits

Experience building and owning internal platforms/tools that increase leverage for a non-engineering team (research, operations, support, data/workforce management, or similar) — not solely external-customer-facing product work

Strong Plus

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 (vetting, QC, payments)

Experience with inter-annotator agreement or statistical agreement metrics

Notification and delivery systems experience — Slack bot integrations, transactional email, and idempotent delivery guarantees

Experience designing abstractions over heterogeneous data sources with different consistency guarantees — e.g. a fully-replayable event history vs. an observe-only current-state API requiring synthesized diffing — behind one common interface

Experience building ChatOps-style automation — Slack or GitHub PR-comment bot commands that trigger backend workflows or CI/CD runs

Experience implementing short-lived, rotatable service-to-service JWT auth (key-ID-based rotation, replay-protected tokens, fail-fast config validation) alongside a separate human-facing SSO flow in a paired service

Comfort owning both sides of a system with genuinely different runtimes without a large team to lean on

Prior experience as the primary or sole engineer on a small, high-leverage internal platform

Bonus

Experience with LLM-as-judge or LLM-based QA/review pipelines

Familiarity with OpenTelemetry or comparable observability instrumentation in Go services

Familiarity with LLM provider gateway/routing services (OpenRouter or comparable) — model aliasing, rate-limit and timeout handling, and budget enforcement

Experience with data export/reporting tools (Excel generation, CSV pipelines, or BI-style dashboards)

Our Story

Our founders, Spence and John met at Google working on Google Translate. As researchers at Stanford and Berkeley, they both worked on language technology to make information accessible to everyone. While together at Google, they were amazed to learn that Google Translate wasn’t used for enterprise products a