Tech Lead — ASR​/TTS​/Speech LLM; IC + Mentor

About the Company

OutcomesAI is a healthcare technology company building an AI-enabled nursing platform designed to augment clinical teams, automate routine workflows, and safely scale nursing capacity. Our solution combines AI voice agents and licensed nurses to handle patient communication, symptom triage, remote monitoring, and post-acute care — reducing administrative burden and enabling clinicians to focus on direct patient care.

Our core product suite includes:

• Glia Voice Agents – multimodal conversational agents capable of answering patient calls, triaging symptoms using evidence-based protocols (e.g., Schmitt‑Thompson), scheduling visits, and delivering education and follow‑ups
• Glia Productivity Agents – AI copilots for nurses that automate charting, scribing, and clinical decision support by integrating directly into EHR systems such as Epic and Athena
• AI‑Enabled Nursing Services – a hybrid care delivery model where AI and licensed nurses work together to deliver virtual triage, remote patient monitoring, and specialty patient support programs (e.g., oncology, dementia, dialysis)

Our AI infrastructure leverages multimodal foundation models — incorporating speech recognition (ASR), natural language understanding, and text‑to‑speech (TTS) — fine‑tuned for healthcare environments to ensure safety, empathy, and clinical accuracy. All models operate within a HIPAA‑compliant and SOC 2‑certified framework. Outcomes

AI partners with leading health systems and virtual care organizations to deploy and validate these capabilities  goal is to create the world’s first AI + nurse hybrid workforce
, improving access, safety, and efficiency across the continuum of care.

You’ll 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. As a Tech Lead specializing in ASR, TTS, and Speech LLM, you will spearhead the technical development of speech models. This involves everything from architectural design and training strategies to evaluation and production deployment.

• Own the technical roadmap for STT/TTS/Speech LLM model training: from model selection → fine‑tuning → deployment
• Evaluate and benchmark open‑source models (Parakeet, Whisper, etc.) using internal test sets for WER, latency, and entity accuracy
• Design and review data pipelines for synthetic and real data generation (text selection, speaker selection, voice synthesis, noise/distortion augmentation)
• Architect and optimize training recipes (LoRA/adapters, RNN‑T, multi‑objective CTC + MWER)
• Lead integration with Triton Inference Server (Tensor
  
  RT/FP16) and ensure K8s autoscaling for 1000+ concurrent streams
• Implement Language Model biasing APIs, WFST grammars, and context biasing for domain accuracy
• Guide evaluation cycles, drift monitoring, and model switcher/failover strategies
• Mentor engineers on data curation, fine‑tuning, and model serving best practices
• Collaborate with backend/ML‑ops for production readiness, observability, and health metrics

• 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 Tensor
  
  RT optimization
• Telephony robustness:
  Codec augmentation (G.711 μ‑law, Opus, packet loss/jitter), AGC/loudness norm, band‑limit (300–3400 Hz), far‑field/noise simulation
• Strong understanding of telephony noise, codecs, and real‑world audio variability
• Experience in Speaker Diarization, turn detection model, smart voice activity detection
• Evaluation: WER/latency curves, Entity‑F1 (names/DOB/meds), confidence metrics
• TTS: VITS/Fast Pitch/Glow‑TTS/Grad‑TTS/Style
  
  TTS2, Cosy Voice/Natural Speech‑3 style transfer, BigVGAN/Univ Net vocoders, zero‑shot cloning
• Speech LLM:
  Model development and integration with Voice agent pipeline
• Experience deploying models with Triton Inference Server, Kubernetes, and GPU scaling
• Hands‑on with evaluation metrics (WER, F1 on entities, latency p50/p95)
• Familiarity with LM biasing, WFST…