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Audio Speech Machine Learning Jobs (NOW HIRING)

Bland

Machine Learning Researcher, Audio

San Francisco, CA · On-site

$140K - $250K/yr

Machine Learning Researcher, Audio Location: San Francisco, CA or Remote About Bland At Bland.com ... Design and train large scale text-to-speech models capable of expressive, controllable, human ...

GTN Technical Staffing

Be Seen First

Speech AI Researcher/Engineer

San Francisco, CA · On-site

$160K - $250K/yr

The role is ideal for someone who has deep experience with modern machine learning for audio, speech, and language, and who enjoys moving beyond prototypes into systems that must perform reliably in ...

HARMAN International

Audio ML Engineer (Research)

The Audio ML Engineer (Research) will develop machine learning models to enhance Intelligent Audio ... Preferred : • Experience with audio ML domains (speech enhancement, denoising, source separation ...

GN Group

Machine Learning Engineer - Edge

Lowell, MA · On-site +1

$86K - $135K/yr

Machine Learning Engineer - Edge *Please consider before applying: This is a hybrid role, and ... Fundamentals of audio and speech signal processing. Pay Transparency Notice * Depending on your ...

GN Group

Machine Learning Engineer - Edge

Dover, NH · On-site +1

$86K - $135K/yr

Machine Learning Engineer - Edge *Please consider before applying: This is a hybrid role, and ... Fundamentals of audio and speech signal processing. Pay Transparency Notice * Depending on your ...

GN Group

Machine Learning Engineer - Edge

Dover, NH · On-site

$86K - $135K/yr

Machine Learning Engineer - Edge *Please consider before applying: This is a hybrid role, and ... Fundamentals of audio and speech signal processing. Pay Transparency Notice * Depending on your ...

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All JobsAudio Speech Machine Learning Jobs

Audio Speech Machine Learning information

What are some common challenges faced when developing machine learning models for audio speech applications?

A key challenge in audio speech machine learning roles is dealing with diverse and noisy audio data, which can significantly affect model accuracy. Additionally, models must be robust to different accents, languages, and speaking styles, requiring large and varied datasets for training and validation. Collaboration with data engineers, linguists, and software developers is often necessary to ensure high-quality data pipelines and model integration into production systems. Staying updated with the latest research and optimizing models for real-time performance are also ongoing aspects of the role.

What is an Audio Speech Machine Learning Engineer?

An Audio Speech Machine Learning Engineer is a specialized professional who designs, develops, and implements machine learning models that process and analyze audio and speech data. Their work involves tasks like speech recognition, speaker identification, and audio event detection by leveraging algorithms and large datasets. These engineers collaborate with data scientists, software developers, and linguists to create applications such as voice assistants, transcription tools, and automated customer service systems. Expertise in signal processing, deep learning frameworks, and programming languages like Python is crucial for this role.

What is the difference between Audio Speech Machine Learning vs Speech Data Analyst?

AspectAudio Speech Machine LearningSpeech Data Analyst
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegree in Data Analysis, Statistics, or related fields; experience with data tools
Work EnvironmentResearch labs, tech companies, AI startupsData analysis teams, research institutions, tech firms
Industry UsageDeveloping speech recognition, voice assistants, NLP applicationsAnalyzing speech datasets, improving speech models, reporting insights

Audio Speech Machine Learning focuses on developing algorithms for speech recognition and processing, often involving model training and AI development. Speech Data Analysts interpret speech data, generate insights, and support model improvements. Both roles require strong analytical skills, but their core tasks differ: one builds models, the other analyzes data.

What are the key skills and qualifications needed to thrive as an Audio Speech Machine Learning Engineer, and why are they important?

To thrive as an Audio Speech Machine Learning Engineer, you need a solid background in machine learning, signal processing, and programming (typically Python), along with a relevant degree in computer science or a related field. Familiarity with tools like TensorFlow or PyTorch, audio processing libraries (such as Librosa), and experience with speech datasets and ASR systems are commonly required. Critical soft skills include problem-solving, innovation, and effective communication for collaborating with cross-functional teams. These skills are essential to develop accurate, scalable speech recognition systems that advance voice-driven technology.
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Audio Speech Machine Learning jobs near you
Research Engineer - Audio & Speech Models

Research Engineer - Audio & Speech Models

Zyphra Technologies Inc

San Francisco, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 16 days ago

Job description

Zyphra is an artificial intelligence company based in San Francisco, California.
The Role:
As a Research Engineer - Audio & Speech Models, you will be a core contributor on Zyphra's Audio Team, building the next generation of open-source autoencoders, ASR, TTS, SSL, and speech-to-speech models. You will be deeply involved in the entire model training process, from data gathering and processing to designing novel architectures and training methodologies.
You'll Work Across:
  • Large-scale audio training runs
  • Performance optimization of our training stack
  • Audio dataset collection, processing, and evaluation
  • Architecture and training methodology ablations and improvements

What We're Looking For / Requirements:
  • Strong research taste and intuition. The ability to work through a research project from conception to execution to write-up.
  • Strong implementation and prototyping ability (can take an idea from conception to experimentation quickly)
  • The ability to work well with others in a high-paced research setting
  • Excellent communication and collaboration skills, and can work effectively on both research and engineering implementation at scale

Qualifications / Additional Skills:
  • Expertise and intuition for training models in the audio domain, including text-to-speech, ASR, speech-to-speech, speech-emotion-recognition, or other models
  • Experience in training audio autoencoders
  • Understanding of signal processing, especially of audio signals
  • Experience with diffusion models, consistency models, or GANs
  • Experience with training on large-scale (multi-node) GPU clusters
  • Strong grasp of proper experimental methodology for running rigorous ablations and other hypothesis testing
  • Understanding of and interest in large-scale, highly parallel data processing pipelines
  • Proficiency with PyTorch and Python
  • Experience contributing to large pre-existing codebases and rapidly getting up to speed
  • Previously published machine learning research in well-respected venues
  • Postgraduate degree in a scientific subject (Computer Science, EE/EECS, Mathematics, Physics, Machine Learning)

Why Work at Zyphra:
  • Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued
  • We strongly value new and crazy ideas and are very willing to bet big on new ideas
  • We move as quickly as we can; we aim to minimize the bar to impact as low as possible
  • We all enjoy what we do and love discussing AI

Benefits and Perks:
  • Comprehensive medical, dental, vision, and FSA plans
  • Competitive compensation and 401(k) plan
  • Relocation and immigration support on a case-by-case basis
  • In-office snacks and meals provided
  • Unlimited PTO and company holidays
  • In-person team in San Francisco with a collaborative, high-energy environment

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