Job description

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 linkedin.com, Canva linkedin.com, the United States Department of Defense linkedin.com, the United States Air Force linkedin.com, ASICS linkedin.com, 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. Lilt's Converse product delivers real-time, multi-party translation for high-stakes conversations — from offline, device-based deployments in the field to live meetings on enterprise collaboration platforms. We're expanding Converse to support any number of participants, speaking any combination of languages, joining from a growing set of form factors — with live translation delivered N-way, directly on the meeting stage, and cleared interpreters reviewing only the segments that need a human eye. You will build and operate the backend services that make this work: per-participant audio ingestion, real-time diarization and translation orchestration, and the routing logic that gets a flagged segment in front of the right interpreter and broadcasts their correction back out in real time. This is a ground-floor opportunity to help build a brand-new product surface, on a team whose existing products serve some of the world's largest global brands. As a Backend Engineer on the Create team, you'll work closely with senior engineers and Product to build the services powering this Converse expansion — the real-time translation pipeline and the meeting-platform integration — taking well-scoped services and features from design through production with support and code review from more senior teammates. You'll help define clean API contracts with the frontend/interpreter-workbench engineers, contribute to the event-driven correction and broadcast system, and grow into owning larger pieces of the backend architecture as the product and team scale. Location & eligibility: This position requires US citizenship and residence in the United States. Preferred locations are Washington, D.C.; Boston, MA; and Indianapolis, IN. WHAT YOU'LL DO - Build and maintain backend services for the real-time translation pipeline: audio ingestion, diarization hookups, streaming ASR/MT orchestration, and result fan-out to every configured meeting language - Implement the event-driven routing logic that flags low-confidence segments and routes them to the correct on-call interpreter by language - Build APIs and services backing the bot/agent participant and meeting side panel that deliver translation into the host meeting platform, with guidance from senior engineers - Contribute to the interpreter correction and broadcast system — ensuring corrections are isolated to the correct language and broadcast to all participants in real time - Write and maintain automated tests (unit, integration, e2e) and contribute to CI/CD pipelines (GitHub Actions) for new backend services - Instrument services for observability — logging, metrics, tracing — to support debugging of latency-sensitive, multi-party sessions - Partner with frontend/fullstack engineers on the Create team to define clean API contracts between backend services and the live meeting UI and interpreter workbench - Participate in on-call rotation and help triage production issues as the product moves toward GA - Help generate post-meeting assets: diarized transcripts, exportable formats (TXT/WebVTT/JSON), and per-line verification metadata WHAT WE'RE LOOKING FOR 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 What sets our platform apart: - 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 thesoftwarereport.com Top 100 Software Companies! - LILT makes it onto the Inc. 5000 List labs.lilt.com. - LILT’s continues to be an intellectual powerhouse, holding numerous patents patents.justia.com that help power the most efficient and sophisticated AI and language models in the industry. - Check out all our news on our website lilt.com. Information collected and processed as part of your application process, including any job applications you choose to submit, is subject to LILT's Privacy Policy at lilt.com. 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 recruiting@lilt.com. LILT is an equal opportunity employer. We extend equal opportunity to all individuals without regard to an individual’s race, religion, color, national origin, ancestry, sex, sexual orientation, gender identity, age, physical or mental disability, medical condition, genetic characteristics, veteran or marital status, pregnancy, or any other classification protected by applicable local, state or federal laws. We are committed to the principles of fair employment and the elimination of all discriminatory practices.

Requirements

Must-haves

Build and maintain backend services for the real-time translation pipeline: audio ingestion, diarization hookups, streaming ASR/MT orchestration, and result fan-out to every configured meeting language
Implement the event-driven routing logic that flags low-confidence segments and routes them to the correct on-call interpreter by language
Build APIs and services backing the bot/agent participant and meeting side panel that deliver translation into the host meeting platform, with guidance from senior engineers
Contribute to the interpreter correction and broadcast system — ensuring corrections are isolated to the correct language and broadcast to all participants in real time
Write and maintain automated tests (unit, integration, e2e) and contribute to CI/CD pipelines (GitHub Actions) for new backend services
Instrument services for observability — logging, metrics, tracing — to support debugging of latency-sensitive, multi-party sessions
Partner with frontend/fullstack engineers on the Create team to define clean API contracts between backend services and the live meeting UI and interpreter workbench
Participate in on-call rotation and help triage production issues as the product moves toward GA
Help generate post-meeting assets: diarized transcripts, exportable formats (TXT/WebVTT/JSON), and per-line verification metadata
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
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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapici/cdrestnode.jstypescriptjavapythongraphqlreactazuremlmachine learning

Platform: ashby
Effort: quick

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