speech-to-speech Jobs

Innodata is a global data engineering company focused on the intersection of data and Artificial Intelligence (AI). Our mission is to enable the responsible advancement of AI by providing the data, evaluation frameworks, and human expertise needed to build trustworthy AI systems at scale. We offer a range of solutions, platforms, and services for Generative AI builders and adopters, leveraging over 36 years of experience in delivering high-quality data and outstanding customer outcomes. This role focuses on the science behind speech and audio models, addressing challenges in robustness across accents, noise, and code-switching, speaker diarization, speech naturalness, and streaming latency. You will be instrumental in building the data and evaluation designs that drive progress in these areas for customers and frontier labs. Your work will involve critical judgment on benchmark coverage, transcription conventions, and the appropriate use of automated versus human evaluation metrics. You will collaborate closely with our transcription and linguistics lead to ensure our data standards directly influence model learning.

$160k - $185k

Liquid AI, a company spun out of MIT CSAIL, is developing general-purpose AI systems designed for efficient deployment across various targets, from data centers to on-device hardware, prioritizing low latency, minimal memory usage, privacy, and reliability. They are expanding rapidly and seeking exceptional talent to join their team. This role focuses on "LFM2.5-Audio," Liquid's multimodal speech and text language model, which handles speech-to-speech conversation, ASR, and TTS without separate components, making it ideal for real-time, on-device applications. The core challenge for this position is adapting audio models to understand user intents and translate them into structured tool calls, enabling voice-driven function calling for enterprise customers. This position offers a unique opportunity to work at the intersection of advanced audio models and real-world deployment, owning the applied post-training work from data generation to delivery, and directly influencing the evolution of Liquid's post-training and audio stacks.

Liquid AI, a rapidly scaling company spun out of MIT CSAIL, is building general-purpose AI systems designed for efficiency across various deployment targets, from data centers to on-device hardware. These systems prioritize low latency, minimal memory usage, privacy, and reliability for enterprise clients in sectors like consumer electronics, automotive, life sciences, and financial services. The Audio team is developing advanced speech-language models for STT, TTS, and speech-to-speech applications within a unified architecture. This role focuses on applied audio model development, working closely with the technical lead to deliver production systems that operate under real-time, on-device constraints. The position offers high ownership of critical workstreams, including data pipelines, evaluation systems, and customer deployments, within a small, elite team where code directly ships to production.