LILT

AI Research Scientist

• Architect, train, fine-tune, and evaluate state-of-the-art speech representations and ASR models (e.g., End-to-End Conformer, Whisper, RNN-T, and hybrid CTC/Attention architectures) across multiple global languages
• Design and deploy highly scalable algorithms for dynamic vocabulary insertion, contextual biasing, and language model (LM) personalization to precisely capture customer-specific terminology, acronyms, and product names
• Implement automated framework evaluations to benchmark model performance, rigorously tracking Word Error Rate (WER), Character Error Rate (CER), embedding-based metrics, latency budgets (RTF), and computing efficiency profiles under varying acoustic environments
• Develop pioneering multilingual benchmarks for end-to-end conversational AI agents, including speech-to-text and text-to-speech components
• Partner with core engineering teams to build, optimize, and maintain high-throughput pipelines optimized for both ultra-low latency real-time streaming inference and high-efficiency asynchronous (batch) multi-channel speech analysis
• Translate product requirements into technical AI roadmaps, working hand-in-hand with Product Managers to ship speech-to-text, simultaneous translation, and semantic speech analytics features

AI Researcher / ML Engineer – ASR, Speech Specialist

Human Resources

• Supports the execution of core people programs and processes across the employee lifecycle.
• Responsible for day-to-day administration of benefits, onboarding, offboarding, leave management, compliance, and employee inquiries.
• Building depth across HR functional areas, developing judgment on routine to moderately complex situations.
• Growing into a trusted resource for employees and managers.

HR Generalist

Application Engineer

• Build LILT Converse, an on-device instant translation application
• Work at the intersection of on-device AI and polished mobile UX
• Own the Android application end-to-end, from architecture to performance tuning
• Collaborate closely with ML, product, and design teams
• Architect new features and integrate on-device ML models
• Optimize performance and manage large local model assets
• Ensure reliability in real-world conditions

Android Application Engineer

Technical Program Manager

• Frontier Lab Program Management: Lead the end-to-end execution of high-priority technical programs for frontier AI labs, managing the delivery of massive-scale datasets and bespoke engineering milestones.
• High-Volume Data Operations: Architect and oversee pipelines for delivering high-volume data samples across multiple modalities (text, image, video, audio), ensuring strict adherence to delivery cadences without sacrificing precision.
• End-to-End Quality Control: Partner with production to implement rigorous, automated, and human-in-the-loop QC frameworks to ensure data integrity across various domains and languages.
• Bespoke FDE Deliverables: Lead technical workstreams to deliver custom research and engineering improvements via the forward-deployed engineers, such as RAG (Retrieval-Augmented Generation) workflow optimizations, model evaluations, and synthetic data generation.
• RL & Environment Management: Oversee projects involving Reinforcement Learning (RL) environments, ensuring the infrastructure and human-feedback loops are optimized for model alignment and performance.
• Technical Consultation: Serve as the primary technical point of contact for AI researchers and engineers at partner labs, translating complex research needs into actionable engineering roadmaps.
• Cross-Functional Leadership: Bridge the gap between forward-deployed engineering resources, research, and production teams to ensure LILT can serve the needs of the frontier AI market.

Senior Technical Program Manager, Applied AI

Account Executive

• LILT is growing quickly, and the Enterprise Account Executive role is core to our success.
• You will achieve this by selling our platform and services to new customers while also supporting our Customer Success Managers to expand partnerships with larger customers.
• Close your quota through your own outbound efforts.
• Uncover, educate, and close prospective customers on time and on forecast that meet company metrics and objectives.
• Become an expert in translating the value of our platform and services to all levels and across all functions, including C-level executives, into the language and metrics of the customer in order to sell the outcome impact to business buyers.
• Set-up and deliver winning POCs in order to convert new, long-lasting customers.
• Provide a weekly accounting of your high-gain activities for continuous improvement as well as maintaining an accurate forecast of your total business.
• Sustain high activity levels across all channels employing tailored, multi-thread outreach.

Enterprise Account Executive

Solutions Engineer

• Serve as the technical co-pilot to LILT Account Executives and Account Managers, managing the customer's technical experience from scoping (pre-sales) to deployment (post-sales / success)
• Build and present reference architectures, how-tos, and demo applications for customers
• Be a subject-matter expert in translation and localization process optimization and best practices
• Understand and align the customer's technology stack to optimize localization processes
• Work cross-functionally with Engineering, Product, and Account Management teams to ensure smooth prospect-to-customer transitions and long-term customer success
• Play an active role in the development and maintenance of customer integrations
• Provide an escalated level of support for critical customer operational issues
• Develop and iterate on internal processes to ensure consistency across our solutions engagements
• Track existing technology solutions and follow the latest trends and developments relevant to multilingual content management and translation, specifically the application of AI

Solutions Architect

• Evaluate & Benchmark: Run rigorous evaluations on frontier LLMs and autonomous agents across diverse tasks.
• Data Engineering: Create or modify benchmark data to test the reasoning and linguistic limits of modern AI.
• Experimental Research: Design and run experiments to identify "model-breaking points" and interpret the resulting data.