LILT

Backend Engineer

• Build and maintain backend services for the real-time translation pipeline, including audio ingestion, diarization hookups, streaming ASR/MT orchestration, and result fan-out
• Implement event-driven routing logic for low-confidence segments and route them to the correct on-call interpreter by language
• Build APIs and services for the bot/agent participant and meeting side panel
• Contribute to interpreter correction and real-time broadcast systems
• Write and maintain unit, integration, and end-to-end tests
• Contribute to GitHub Actions CI/CD pipelines
• Instrument services with logging, metrics, and tracing for observability
• Partner with frontend/fullstack engineers to define API contracts for the live meeting UI and interpreter workbench
• Participate in the on-call rotation and triage production issues
• Generate post-meeting assets, including diarized transcripts, TXT/WebVTT/JSON exports, and per-line verification metadata
• Work with senior engineers and Product to take services and features from design through production

Full-stack Engineer

• Own architecture and technical roadmap for the Converse expansion across real-time translation services and customer-facing surfaces
• Architect and build low-latency, N-way translation pipelines with audio ingestion, diarization, streaming ASR, MT orchestration, and multilingual result fan-out
• Build live meeting experiences with bot/agent participants, side panels, and on-stage captions using meeting-platform SDKs and APIs
• Build an interpreter workbench with live segment queues, low-confidence flagging, TM/TB matches, AI-Review/AI-QA signals, and broadcast corrections
• Design event-driven correction and broadcast systems routing flagged segments to interpreters and isolating corrections by language
• Partner with Product and Design to turn PRDs into technical designs and deliver a new product surface
• Extend shared translation-service architecture to multi-device and platform form factors
• Establish testing, CI/CD, and release practices using GitHub Actions and automated end-to-end tests
• Recruit, mentor, and set engineering standards as the team scales

Senior Full Stack Engineer

Machine Learning Engineer

• Build and manage high-throughput, real-time audio and text streaming services
• Handle signal processing, session lifecycles, and concurrency management
• Integrate and serve streaming speech recognition and machine translation models
• Develop confidence scoring, human-intervention routing, and real-time corrections
• Architect and scale GPU-accelerated Kubernetes infrastructure
• Implement batching, load balancing, and autoscaling strategies
• Instrument performance, identify bottlenecks, optimize throughput, and reduce latency
• Define audio ingestion and downstream integration APIs and technical contracts
• Collaborate with research, frontend, platform engineering, and product teams

Machine Learning Engineer – Real-Time Speech Translation

Technical Program Manager

• Proactively propose and co-develop benchmark transcreations with research lab partners
• Lead end-to-end execution of high-priority technical programs for frontier AI labs
• Manage delivery of massive-scale datasets and bespoke engineering milestones
• Architect and oversee high-volume data pipelines across text, image, video, and audio modalities
• Maintain strict delivery cadences while ensuring precision
• Partner with production to implement automated and human-in-the-loop quality-control frameworks
• Ensure data integrity across domains and languages
• Bridge forward-deployed engineering, research, and production teams to serve the frontier AI market

Senior Technical Program Manager – Applied AI

Technical Product Manager

• Own connector strategy and roadmap, setting priorities and sequencing delivery
• Lead new connectors and capabilities from discovery through launch
• Define requirements, validate prerequisites, establish acceptance criteria, and coordinate delivery and customer readiness
• Turn customer implementations into scalable products with Forward-Deployed Engineering
• Evaluate technical feasibility through third-party documentation and integration assessments
• Own validation and acceptance testing, including integration behavior, file and tag handling, and customer requirements
• Drive connector portfolio reliability by prioritizing issues and coordinating resolution
• Lead technical customer conversations covering discovery, setup planning, scoping, troubleshooting, and readiness
• Create documentation, runbooks, and guidance for implementation and support teams
• Maintain visibility into connector status, risks, ownership, priorities, and customer impact
• Use AI tools and agentic workflows to improve research, scoping, testing, documentation, and troubleshooting
• Collaborate with Engineering, Solutions, Forward-Deployed Engineering, Customer Engineering, Customer Success, and customers

Technical Product Manager – Connectors

Technical Project Manager

• Lead large-scale multilingual data collection, human-in-the-loop workflows, and LLM evaluation initiatives
• Manage Applied AI data collection and evaluation projects from technical scoping through implementation, validation, delivery, and retrospective review
• Translate technical AI requirements into structured data specifications, annotation requirements, evaluation plans, and acceptance criteria
• Manage timelines, throughput velocity, dependencies, delivery risks, and technical blockers
• Coordinate data collection and annotation for SFT, RLHF preference data, and model evaluation
• Partner with engineering teams to define and validate ingestion, annotation tooling, integrations, quality checks, data generation pipelines, and delivery workflows
• Operationalize hybrid evaluation methodologies combining human review, automated evaluation tooling, and LLM-as-a-judge frameworks
• Investigate data, pipeline, and tooling issues and coordinate resolution with technical owners
• Analyze throughput velocity, quality, unit economics, and supplier/delivery performance using SQL and business intelligence tools
• Define and monitor quality standards, lead root-cause analysis, and establish delivery validation checks
• Coordinate stakeholder acceptance of completed datasets, delivery targets, and evaluation results
• Translate complex technical requirements into actionable plans for global contributors, vendors, and specialized SMEs
• Communicate project status, risks, tradeoffs, unit economics, and operational risks to technical and non-technical stakeholders
• Use contributor feedback and evaluation findings to improve internal tooling, guidelines, and dataset pipelines