layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Backend Engineer at LILT

Required
2–5 years of professional backend software engineering experience, ideally including production systems that handle real-time or streaming data
Experience building REST API endpoints as well as streaming services with WebSockets, gRPC, message queues, or comparable low-latency/event-driven patterns
Solid experience with Node.js/TypeScript, Java, Python, or a comparable backend language/ecosystem, and the ability to work across more than one when needed
Experience building and consuming REST/GraphQL APIs, including authentication patterns (OAuth, API keys/tokens, service accounts)
Comfort with relational/NoSQL data modeling for session state, transcripts, and configuration data
Solid grasp of testing and delivery practices: CI/CD and automated integration/e2e tests
Ability to take a well-scoped ticket or spec and ship it with minimal oversight, while communicating blockers and timeline risk early
US citizenship and residence in the United States
Strong Plus
Experience with speech/audio pipelines: diarization, streaming ASR, or audio stream handling
Experience integrating with third-party platform APIs, SDKs, or webhooks (Slack, Zoom, or similar collaboration platforms)
Familiarity with localization/TMS workflows: XLIFF, Translation Memory, MT vs. human translation pipelines
Experience with event-driven/pub-sub architectures (Kafka, Pub/Sub, or similar)
Enough frontend/React exposure to reason about API contracts with a real-time, stateful UI
Bonus
Hands-on Microsoft Teams app or bot development specifically (Teams Toolkit, Bot Framework, Azure Communication Services, Microsoft Graph)
Experience working on 0-to-1 products within a larger, established company
Interest or coursework in NLP/ML systems
Experience with observability tooling (e.g., Datadog) in production, latency-sensitive systems
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

Backend Engineer

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