Tech Lead — ASR / TTS / Speech LLM (IC + Mentor)
Tasks
- Apply WFST grammars for domain accuracy
- Architect and optimize training recipes
- Collaborate on production readiness observability and health metrics
- Design synthetic and real data pipelines
- Evaluate and benchmark open source speech models
- Implement language model biasing APIs and context biasing
- Implement model switcher and failover strategies
- Integrate with Triton Inference Server
- Lead end to end speech model development
- Lead evaluation cycles and drift monitoring
- Mentor engineers on data curation and model serving
- Optimize inference with TensorRT
- Own technical roadmap for ASR TTS and speech LLM
Perks/Benefits
Skills/Tech-stack
AGC | Active Learning | Activity Detection | Adapters | Audio augmentation | Automatic Speech Recognition | Bandpass filtering | Bias Mitigation | CTC | Context injection | Data Generation | Drift monitoring | ESPnet | Entity Extraction | Entity recognition | F1-score | FP16 | Failover | Fairseq | Far field | Far field audio | Far field noise simulation | G711 mu law | GPU scaling | Inference Optimization | Inference Server | K8s autoscaling | Kubernetes | LM biasing | Latency optimization | LoRA | Loudness normalization | Model Evaluation | Mu law | NVIDIA Nemo | Noise simulation | Opus | PyTorch | RNN-T | Smart voice activity detection | Speaker Diarization | Speech LLM | Speech Recognition | Streaming RNN T | Synthetic Data Generation | Synthetic audio augmentation | Synthetic data | Telephony codec augmentation | TensorRT | Text to Speech | Triton Inference | Triton Inference Server | Voice Activity Detection | WER | WFST
Education
Roles
Engineer | Lead | Learning Engineer | Machine Learning Engineer | Tech Lead
Regions
Countries
States
Cities
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