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Tech Lead – ASR, TTS, Speech LLM
OutcomesAITech Lead specializing in ASR, TTS, and Speech LLM at OutcomesAI, a healthcare technology company. Leading technical development of speech models to enhance clinical capacity and efficiency.
Core Competencies
Role fitCore 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 architecture, training strategies, and production readiness. Proven ability to mentor teams and guide technical roadmaps while ensuring high performance and accuracy in healthcare applications.
Highest-signal resume keywords
Speech Model DevelopmentASR/TTS Production ExperienceDeep Learning Frameworks (PyTorch, NeMo)Triton Inference Server DeploymentTelephony Robustness Expertise
ATS Keywords
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Hard Skills
ASRTTSSpeech LLMRNN-TCTC ArchitecturesLoRA/AdaptersTensorRT OptimizationSpeaker DiarizationEvaluation Metrics (WER, F1)Model Fine-Tuning
Soft Skills
MentorshipCollaborationCode Review Discipline
Tools & Technologies
Triton Inference ServerKubernetesGPU ScalingWFST GrammarsContext Injection
Certifications & Qualifications
M.S. / Ph.D. in Computer ScienceSpeech Processing
Industry Keywords
Healthcare ApplicationsSynthetic Data GenerationActive LearningInference OptimizationTelephony Noise
Tech Stack
Tools & technologiesKubernetesPyTorch
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: 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 (TensorRT/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.
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 detection Evaluation: 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- Health insurance
- Professional development opportunities
- Flexible working hours