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Audio Speech Machine Learning Jobs (NOW HIRING)

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

Design, train, evaluate, and deploy machine learning models across text, image, audio, and ... LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic ...

femtoAI

Audio Deep Learning Engineer

San Bruno, CA · On-site

Develop and optimize deep learning models for audio processing, including tasks like speech ... Desired Skills and Experience Deep learning, Machine learning, DSP, Python, PyTorch Benefits ...

Netflix

OR · On-site

$523K - $920K/yr

Responsibilities Lead a broad portfolio of end-to-end initiatives in multimodal LLM and audio ... Highly proficient in multimodal LLM and speech algorithm research, and deeply committed to staying ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

Design, train, evaluate, and deploy machine learning models across text, image, audio, and ... LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic ...

Adobe

Senior Research Scientist

San Francisco, CA · On-site

$116K - $147K/yr

Deep expertise in audio and machine learning, including strong intuition for: * Speech and audio generation * Audio representations and modeling * Training large-scale neural models * Hands-on ...

Apex Informatics

Machine Learning Engineer

Applied Machine Learning Engineer | Music Software (Multiple Roles open) Role: Applied Machine ... audio and other unstructured data. • Collaborate with Product and Engineering teams to ensure ...

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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
Infographic showing various Audio Speech Machine Learning job openings in the United States as of May 2026, with employment types broken down into 50% Internship, and 50% Full Time. Highlights an 100% In-person job distribution.
Sr. Machine Learning Scientist, Siri Speech

Sr. Machine Learning Scientist, Siri Speech

Apple

Cupertino, CA • On-site

Full-time

Posted 13 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

The Speech Team within the Siri organization drives major speech recognition, synthesis and speech to speech model changes for various features deeply embedded throughout Apple's ecosystem. Our mission is to build cutting-edge infrastructure, datasets, and models that empower Siri conversational AI, dictation and various speech enabled Apple Intelligence features with powerful capabilities across natural language understanding, dialog generation, speech recognition, and multi-modal interaction. We apply these technologies to create engaging, intelligent, and personalized conversational experiences for millions of Apple users. ..We believe that the most impactful breakthroughs in deep learning emerge when we address real-world problems at scale. We develop speech to speech experiences and the underlying multimodal foundation model technology for current and future speech-enabled features across Apple's software, hardware, and services ecosystem. This allows for cutting edge applied research anchored in Apple specific production needs, while improving speech interaction experiences for Apple's customers around the world.
You will work alongside a fast-growing team of world-class engineers and scientists to tackle core problems in dialog systems and foundation models- ranging from natural language understanding and multi-turn context tracking, to the integration of speech, text, and other modalities. You will develop and deploy novel deep learning technologies that make Siri more intelligent, natural, and useful. You'll help us advance the state of the art in natural language processing, speech and audio modeling, and multi-modal learning, with a strong focus on bringing your innovations into production. Your ideas will directly impact the daily lives of billions of users through Siri.
Demonstrated expertise in deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, KDD, ACL, ICASSP, InterSpeech) or a track record in applying deep learning techniques to productsProficient programming skills in Python and one of the deep learning toolkits such as PyTorch, JAX, or Tensorflow
Bachelor's, Master's, or PhD in Computer Science or other related discliplineExperience with conversational AI or multimodal LLMExperience with large scale machine learning training/evaluationData-centric vision about foundation model

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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