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Speech Recognition Trainer Jobs (NOW HIRING)

Deep experience with automatic speech recognition (ASR) * Experience training or fine-tuning Whisper, Conformer, wav2vec, or similar speech architectures * Experience with large-scale audio datasets ...

Deep experience with automatic speech recognition (ASR) * Experience training or fine-tuning Whisper, Conformer, wav2vec, or similar speech architectures * Experience with large-scale audio datasets ...

Knowledge of audio tokenization, neural audio codecs, streaming speech recognition, streaming speech generation, or low-latency speech architectures * Experience with distributed training and ...

Knowledge of audio tokenization, neural audio codecs, streaming speech recognition, streaming speech generation, or low-latency speech architectures * Experience with distributed training and ...

Knowledge of audio tokenization, neural audio codecs, streaming speech recognition, streaming speech generation, or low-latency speech architectures * Experience with distributed training and ...

Knowledge of audio tokenization, neural audio codecs, streaming speech recognition, streaming speech generation, or low-latency speech architectures * Experience with distributed training and ...

Knowledge of audio tokenization, neural audio codecs, streaming speech recognition, streaming speech generation, or low-latency speech architectures * Experience with distributed training and ...

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How much do speech recognition trainer jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for speech recognition trainer in the United States is $24.74, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $26.44 per hour, depending on experience, location, and employer.

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Infographic showing various Speech Recognition Trainer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 59% Physical, 1% Hybrid, and 40% Remote job distribution, with an average salary of $51,453 per year, or $24.7 per hour.

Speech Recognition Engineer

Manhattan, NY โ€ข On-site

Other

Re-posted 4 days ago


Job description

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