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Speech Recognition Project Manager Jobs (NOW HIRING)

DabKick

Speech Recognition Engineer

San Jose, CA · On-site

Do you have the passion and drive in working on complex projects within a fast-paced startup ... manage without authorit Solid Machine Learning background and familiar with standard speech and ...

DabKick

Speech Recognition Engineer

San Jose, CA · On-site

Do you have the passion and drive in working on complex projects within a fast-paced startup ... manage without authorit Solid Machine Learning background and familiar with standard speech and ...

Reece Group

Project Manager

Houston, TX · On-site

Your project management skills will be used to maintain project schedules and formally respond to ... Oral Communication Skills - Speaking, Speech Clarity, and Speech Recognition. * Written ...

Reece Group

Project Manager

Winston Salem, NC · On-site

Your project management skills will be used to maintain project schedules and formally respond to ... Oral Communication Skills - Speaking, Speech Clarity, and Speech Recognition. * Written ...

Reece Group

Project Manager

Winston Salem, NC · On-site

Your project management skills will be used to maintain project schedules and formally respond to ... Oral Communication Skills - Speaking, Speech Clarity, and Speech Recognition. * Written ...

Morsco

As a Project Manager, you will support metering sales by assisting with metering projects from ... Speaking, Speech Clarity, and Speech Recognition.* **Written Communication Skills -** Writing ...

Morsco

Project Manager

Hanes, NC · On-site

Your project management skills will be used to maintain project schedules and formally respond to ... Oral Communication Skills - Speaking, Speech Clarity, and Speech Recognition.Written Communication ...

Acclaim

Technical Project Manager (Voice AI)

Miami, FL · On-site +1

About the role We are currently seeking talented candidates for the Technical Project Manager ... speech recognition systems, and/or telephony technologies. * A customer-first and data-driven ...

Syntricate Technologies

Generative AI

Tampa, FL · On-site

Knowledge of speech recognition and synthesis technologies. • Computer Vision: Experience with ... project management services. Founded in 2004, the company is headquartered in Boston, USA, with a ...

Kiongroup

Project Manager

Summerville, SC · On-site

Recognized project management certificate, i.e., CAPM, PMP, or Prince 2. * Proven experience with project management. * ERP System knowledge (SAP, preferred) * Knowledge of product development ...

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Speech Recognition Project Manager information

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How much do speech recognition project manager jobs pay per year?

As of Sep 9, 2026, the average yearly pay for speech recognition project manager in the United States is $105,026.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,000.00 and $128,500.00 per year, depending on experience, location, and employer.

What are popular job titles related to Speech Recognition Project Manager jobs?

For Speech Recognition Project Manager jobs, the most frequently searched job titles are:

Infographic showing various Speech Recognition Project Manager job openings in the United States as of September 2026, with employment types broken down into 89% Full Time, 4% Part Time, and 7% Contract. Highlights an 89% In-person, 4% Hybrid, and 7% Remote job distribution, with an average salary of $105,026 per year, or $50.5 per hour.

Speech Recognition Engineer

Manhattan, NY • On-site

Ova Technologies
See more jobs at Ova Technologiesexternal

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

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