Tech Lead – ASR, TTS, Speech LLM, IC, Mentor

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🕒 November 10, 2025

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INOP

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2022

🤖 Artificial Intelligence

⚕️ Healthcare Insurance

☁️ SaaS

Artificial Intelligence • Healthcare Insurance • SaaS

INOP is a healthcare technology company that specializes in Artificial Medical Intelligence (AMI). Their flagship product, Glia®, is an advanced AI platform that integrates seamlessly with clinical workflows to assist healthcare teams in various tasks such as triage, documentation, and clinical decision support. By utilizing multi-modal AI capabilities, Glia® aims to enhance patient care efficiency and outcomes while reducing administrative burdens on healthcare professionals.

📋 Description

• Lead the end-to-end technical development of speech models (ASR, TTS, Speech-LLM) — from architecture, training strategy, and evaluation to production deployment. • Act as an individual contributor and mentor, guiding a small team working on model training, synthetic data generation, active learning, and inference optimization for healthcare applications. • Own the technical roadmap for STT/TTS/Speech LLM model training: from model selection → fine-tuning → deployment. • Evaluate and benchmark open-source models using internal test sets for WER, latency, and entity accuracy. • Design and review data pipelines for synthetic and real data generation. • Architect and optimize training recipes and lead integration with Triton Inference Server. • Implement Language Model biasing APIs and guide evaluation cycles.

🎯 Requirements

• M.S. / Ph.D. in Computer Science, Speech Processing, or related field. • 7–10 years of experience in applied ML, at least 3 in speech or multimodal AI. • Track record of shipping production ASR/TTS models or inference systems at scale. • Deep expertise in speech models (ASR, TTS, Speech LLM) and training frameworks (PyTorch, NeMo, ESPnet, Fairseq). • Proven experience with streaming RNN-T / CTC architectures, LoRA/adapters, and TensorRT optimization. • Strong understanding of telephony noise, codecs, and real-world audio variability. • Experience deploying models with Triton Inference Server, Kubernetes, and GPU scaling. • Hands-on with evaluation metrics (WER, F1 on entities, latency p50/p95). • Strong mentorship and code-review discipline.

🏖️ Benefits

• Health insurance • Paid time off • Flexible work arrangements • Professional development opportunities