• 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

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 and services inside the company.The quality just wasn’t there. So they set out to build something better. LILT was born.

LILT has been a machine learning company since its founding in 2015. At the time, machine translation didn’t meet the quality standard for enterprise translations, so LILT assembled a cutting-edge research team tasked with closing that gap. While meeting customer demand for translation services, LILT has prioritized investments in Large Language Models, human-in-the-loop systems, and now agentic AI.

With AI innovation accelerating and enterprise demand growing, the next phase of LILT’s journey is just beginning.

Our Tech

• Brand-aware AI that learns your voice, tone, and terminology to ensure every translation is accurate and consistent
• Agentic AI workflows that automate the entire translation process from content ingestion to quality review to publishing
• 100+ native integrations with systems like Adobe Experience Manager, Webflow, Salesforce, GitHub, and Google Drive to simplify content translation
• Human-in-the-loop reviews via our global network of professional linguists, for high-impact content that requires expert review

LILT in the News

• Featured in The Software Report’s Top 100 Software Companies!
• LILT makes it onto the Inc. 5000 List .
• LILT’s continues to be an intellectual powerhouse, holding numerous patents that help power the most efficient and sophisticated AI and language models in the industry.
• Check out all our news on our website .

At LILT, we are committed to a fair, inclusive, and transparent hiring process. As part of our recruitment efforts, we may use artificial intelligence (AI) and automated tools to assist in the evaluation of applications, including résumé screening, assessment scoring, and interview analysis. These tools are designed to support human decision-making and help us identify qualified candidates efficiently and objectively. All final hiring decisions are made by people. If you have any concerns, require accommodations, or would like to opt-out of the use of AI in our hiring process, please let us know at .