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

at INOP

• Job title: Tech Lead – ASR, TTS, Speech LLM, IC, Mentor
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: Kubernetes, PyTorch
• Location requirements: Hybrid, Boston, United States

About the role

• Tech Lead for ASR, TTS, and Speech LLM development at OutcomesAI's AI-enabled nursing platform. Leading model training and optimization in healthcare applications.

Responsibilities

• 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