Speech Recognition Engineer

Job Title: Speech Recognition Engineer
Job 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