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Entry Level Speech Recognition Engineer Jobs (NOW HIRING)

DabKick

Speech Recognition Engineer

San Jose, CA · On-site

As a Speech Recognition Engineer , you will be responsible for consumer product design for our Advanced Products and Technologies. In this role, you will be involved in modeling techniques to advance ...

DabKick

Speech Recognition Engineer

San Jose, CA · On-site

As a Speech Recognition Engineer , you will be responsible for consumer product design for our Advanced Products and Technologies. In this role, you will be involved in modeling techniques to advance ...

Parlance Corporation

Solutions Engineer

Indiana, PA · Remote

Parlance delivers speech recognition as a managed service. That means we blend intelligent speech ... Description: As a Solutions Engineer at Parlance , you deliver meaningful results through a ...

Parlance Corporation

Solutions Engineer

Indiana, PA · Remote

Parlance delivers speech recognition as a managed service. That means we blend intelligent speech ... Description: As a Solutions Engineer at Parlance , you deliver meaningful results through a ...

Ursus, Inc.

Language Specialist - Speech AI

... Speech Recognition (ASR) and Text-to-Speech (TTS) systems • Develop WFST and Neural Networks ... with engineers and scientists in data collection and development efforts • Write clear and ...

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Entry Level Speech Recognition Engineer information

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How much do entry level speech recognition engineer jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for entry level speech recognition engineer in the United States is $31.31, according to ZipRecruiter salary data. Most workers in this role earn between $27.40 and $34.62 per hour, depending on experience, location, and employer.

What is the difference between Entry Level Speech Recognition Engineer vs Entry Level Machine Learning Engineer?

AspectEntry Level Speech Recognition EngineerEntry Level Machine Learning Engineer
Required CredentialsBachelor's in Computer Science, Electrical Engineering, or related field; knowledge of speech processingBachelor's in Computer Science, Data Science, or related; foundational ML knowledge
Work EnvironmentResearch labs, tech companies, speech tech startupsTech firms, AI startups, research institutions
Industry UsageSpeech recognition, voice assistants, transcription servicesVarious sectors including healthcare, finance, tech

While both roles require a background in computer science and related fields, Entry Level Speech Recognition Engineers focus specifically on speech processing technologies, whereas Entry Level Machine Learning Engineers work broadly across AI applications. The former specializes in speech data and algorithms, while the latter may work on diverse machine learning models across industries.

More about Entry Level Speech Recognition Engineer jobs
What are the most commonly searched types of Speech Recognition Engineer jobs? The most popular types of Speech Recognition Engineer jobs are:
What job categories do people searching Entry Level Speech Recognition Engineer jobs look for? The top searched job categories for Entry Level Speech Recognition Engineer jobs are:
Entry Level Speech Recognition Engineer jobs near you
Infographic showing various Entry Level Speech Recognition Engineer job openings in the United States as of August 2026, with employment types broken down into 2% As Needed, 81% Full Time, 16% Part Time, and 1% Contract. Highlights an 67% Physical, 1% Hybrid, and 32% Remote job distribution, with an average salary of $65,130 per year, or $31.3 per hour.

Speech Recognition Engineer

Ova Technologies

Manhattan, NY • On-site

Other

Re-posted 3 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

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