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