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.

Role Summary

We are building a new live translation product. We are looking for an ML Engineer to build the real-time speech translation backend that powers it.

You will own the real-time speech translation backend end-to-end, from live audio input to translated output. You will build on LILT's production model serving platform (Ray Serve on GPU Kubernetes clusters) and our in-house adaptive machine translation models, working closely with the senior architects of that platform and with our language processing researchers. The ASR and MT models exist. Your job is to make them work together as a low-latency streaming system that holds up in production.

This is a hands-on backend engineering role for those looking to own a real-time ML system from end to end, supported by expert guidance and a clear product vision. Our team adopts an AI-first approach, leveraging agentic coding and AI-driven PR reviews to accelerate development. We combine this with deep technical expertise, requiring not only expert Python proficiency but also a comprehensive understanding of the entire ML stack, from optimizing neural network architectures to managing production infrastructure on Kubernetes, and making informed, cost-aware decisions on hardware selection.

Location & eligibility: This position requires US citizenship and residence in the United States. Preferred locations are Washington, D.C.; Boston, MA; and Indianapolis, IN (East Coast / ET timezone preferred).

Key Responsibilities

Real-time pipeline architecture: Build and manage services for high-throughput, real-time audio and text streaming. Handle signal processing, session lifecycles, and concurrency management to ensure robust operation under load.

ML model integration: Integrate and serve streaming speech recognition and machine translation models, collaborating with research teams to ensure models operate within required latency budgets.

Quality and confidence workflows: Develop logic for model-based confidence scoring, routing segments for human intervention as needed, and broadcasting real-time updates and corrections to end-users.

Infrastructure and scale: Architect and scale production ML infrastructure on GPU-accelerated Kubernetes clusters. Implement batching, load balancing, and autoscaling strategies to maintain performance and cost-efficiency.

Latency engineering: Establish comprehensive instrumentation for real-time performance. Identify bottlenecks, optimize system throughput, and drive down end-to-end latency metrics to meet production standards.

Interface and API definition: Define technical contracts and interfaces for audio ingestion and downstream service integrations. Partner with frontend and platform engineering teams to maintain clean, robust integration points.

Collaboration and technical leadership: Drive cross-team alignment by defining clear API interfaces and technical contracts, facilitating effective communication between engineering and product teams to ensure seamless system integration.