Job Title: Speech Recognition EngineerJob Summary We are seeking a Speech Recognition Engineer to design, develop, and optimize Automatic Speech Recognition (ASR) systems for voice-enabled applications. The ideal candidate will have expertise in speech processing, deep learning, natural language processing (NLP), and machine learning. This role involves building, training, fine-tuning, and deploying speech recognition models that deliver high accuracy, low latency, and robust performance across diverse languages, accents, and acoustic environments.
Key Responsibilities - Design, develop, and optimize Automatic Speech Recognition (ASR) models for production applications.
- Build end-to-end speech processing pipelines, including audio preprocessing, feature extraction, decoding, and post-processing.
- Train, fine-tune, and evaluate speech recognition models using large-scale speech datasets.
- Improve recognition accuracy for multilingual, domain-specific, and noisy audio environments.
- Develop real-time and batch speech recognition solutions.
- Optimize models for latency, throughput, memory efficiency, and inference performance.
- Integrate ASR models into voice assistants, conversational AI systems, call center platforms, and enterprise applications.
- Develop data pipelines for speech data collection, annotation, augmentation, and quality validation.
- Evaluate model performance using industry-standard speech recognition metrics.
- Collaborate with NLP Engineers, Machine Learning Engineers, AI Engineers, Data Scientists, and Product teams.
- Deploy speech recognition models using MLOps and cloud-native deployment practices.
- Monitor production performance and continuously improve model quality.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Speech Technology, or a related field.
- 3+ years of experience in speech recognition, speech processing, machine learning, or AI engineering.
- Strong programming skills in Python.
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Solid understanding of digital signal processing (DSP) fundamentals.
- Experience with speech processing libraries such as SpeechBrain, ESPnet, Hugging Face Transformers, torchaudio, librosa, or Kaldi.
- Experience training and fine-tuning deep learning models.
- Familiarity with Linux development environments, Git, and containerization using Docker.
- Understanding of cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
Preferred Qualifications - Experience with modern ASR architectures such as Whisper, Conformer, wav2vec 2.0, DeepSpeech, or RNN-Transducer (RNN-T).
- Experience deploying speech recognition models using ONNX Runtime, TensorRT, NVIDIA Triton Inference Server, or TorchServe.
- Knowledge of multilingual and low-resource language speech recognition.
- Experience with streaming speech recognition and real-time inference.
- Familiarity with speech enhancement, voice activity detection (VAD), speaker diarization, and keyword spotting.
- Experience with MLOps tools such as MLflow, Kubeflow, or cloud AI platforms.
- Knowledge of Large Language Models (LLMs) for speech understanding and conversational AI.
Technical Skills - Python
- PyTorch
- TensorFlow
- Hugging Face Transformers
- SpeechBrain
- ESPnet
- Kaldi
- torchaudio
- librosa
- Whisper
- wav2vec 2.0
- Conformer
- RNN-T
- ONNX Runtime
- TensorRT
- NVIDIA Triton Inference Server
- TorchServe
- Docker
- Git
- Linux
- AWS / Azure / Google Cloud Platform
Soft Skills - Strong analytical and problem-solving skills
- Excellent communication and collaboration
- Attention to detail
- Ability to work with cross-functional teams
- Continuous learning mindset
- Strong documentation and experimentation practices
Nice to Have - Experience with speech synthesis (Text-to-Speech) or conversational AI platforms
- Knowledge of multilingual ASR evaluation and benchmarking
- Experience with edge AI deployment for speech applications
- Familiarity with model compression, quantization, and inference optimization
- Publications or contributions in speech AI, ASR, or related open-source projects
Key Performance Indicators (KPIs) - Word Error Rate (WER) and Character Error Rate (CER)
- Model inference latency and throughput
- Speech recognition accuracy across languages and accents
- Production model availability and reliability
- Improvement in recognition quality over baseline models
- Successful deployment and adoption of ASR features
- Reduction in production defects and model regressions
Location Hybrid / Remote / On-site (as applicable)
Employment Type Full-time
