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

OutcomesAI

Tech Lead in healthcare technology company developing ASR, TTS, and Speech LLM models. Lead technical development while mentoring a team focused on healthcare applications.

Posted 7/29/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in developing and deploying speech models, including ASR, TTS, and Speech LLM, with a strong focus on production readiness and performance optimization. Proven ability to mentor teams and guide technical roadmaps in healthcare applications.

Highest-signal resume keywords

Speech Model DevelopmentProduction ASR/TTS Model ShippingApplied Machine Learning ExpertiseExperience with PyTorch and NeMoTelephony Robustness and Noise Handling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ASR Model DevelopmentTTS Model DevelopmentSpeech LLM IntegrationStreaming RNN-T ArchitecturesTensorRT OptimizationEvaluation Metrics (WER, F1)Speaker DiarizationTurn Detection ModelSmart Voice Activity DetectionCodec Augmentation

Soft Skills

MentorshipCode Review Discipline

Tools & Technologies

Triton Inference ServerKubernetesGPU ScalingFairseqESPnet

Certifications & Qualifications

M.S. / Ph.D. in Computer ScienceSpeech Processing

Industry Keywords

Healthcare ApplicationsMultimodal AITelephony NoiseReal-World Audio VariabilityContext Injection

Tech Stack

Tools & technologies

KubernetesPyTorch

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.
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 detectionEvaluation: WER/latency curves, Entity-F1 (names/DOB/meds), confidence metrics.
TTS : VITS/FastPitch/Glow-TTS/Grad-TTS/StyleTTS2, CosyVoice/NaturalSpeech-3 style transfer, BigVGAN/UnivNet 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 grammars, and context injection.
Strong mentorship and code-review discipline.

Benefits

Comp & perks

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