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Full Time Audio 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, our mission is to empower enterprises to build AI phone agents at scale. Based in San Francisco, we ...

Spotify

Machine Learning Engineer

Spotify is a leading audio streaming subscription service that aims to unlock the potential of human creativity. They are seeking a Machine Learning Engineer to build systems that analyze the ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

Responsibilities : • Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data. • ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

Responsibilities : • Design, build, and optimize scalable machine learning pipelines for multimodal model training, fine-tuning, and evaluation across text, image, audio, video, and 3D data. • ...

Cohere

Audio Inference Engineer, Model Efficiency

Responsibilities : • Build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. • Advance core audio model serving metrics, including ...

Cohere

Audio Inference Engineer, Model Efficiency

Responsibilities : • Build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. • Advance core audio model serving metrics, including ...

Google

Applied AI Engineer, Audio, XR

Mountain View, CA · On-site

Innovate in audio machine learning through fundamental and applied research, advancing the state-of-the-art in audio playback, capture, generation, and editing. * Research and develop novel ML models ...

Hive

Machine Learning Manager

San Francisco, CA · On-site

$180K - $250K/yr

Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...

Hive

Machine Learning Manager

Seattle, WA · On-site

$180K - $250K/yr

Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...

Hive

Machine Learning Manager

San Francisco, CA · On-site

$180K - $250K/yr

Knowledgeable in at least one focus area of machine learning, such as computer vision, audio, or NLP * 2+ years experience managing machine learning teams * You have an ability to understand and make ...

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 multimodal domains. * Develop and improve classification systems for safety, security, abuse detection, and ...

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All JobsAudio Machine Learning JobsFull Time Audio Machine Learning Jobs

Full Time Audio Machine Learning information

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$29.5K

$84.5K

$171.5K

How much do full time audio machine learning jobs pay per year?

As of Jul 9, 2026, the average yearly pay for full time audio machine learning in the United States is $84,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $113,000.00 per year, depending on experience, location, and employer.

What is the difference between Full Time Audio Machine Learning vs Audio Data Scientist?

AspectFull Time Audio Machine LearningAudio Data Scientist
Required CredentialsDegree in Computer Science, Electrical Engineering, or related field; experience in machine learning and audio processingSimilar credentials; strong background in data science, statistics, and audio analysis
Work EnvironmentResearch labs, tech companies, startups focusing on audio applicationsData-driven teams, analytics departments, R&D units in tech or entertainment industries
Employer & Industry UsageTech firms developing speech recognition, audio enhancement, or sound classificationCompanies analyzing audio data for insights, product development, or quality control

Both roles require expertise in audio processing and machine learning, often sharing similar educational backgrounds. Full Time Audio Machine Learning specialists focus on developing models and algorithms, while Audio Data Scientists analyze audio data to extract insights. The roles are closely related and often overlap, but the former emphasizes model development, whereas the latter emphasizes data analysis and interpretation.

What is a Full Time Audio Machine Learning job?

A Full Time Audio Machine Learning job involves developing and applying machine learning algorithms to process and analyze audio data, such as music, speech, or environmental sounds. Professionals in this role work on tasks like audio classification, speech recognition, sound synthesis, and noise reduction. They often collaborate with data scientists, audio engineers, and software developers to build AI-driven applications for industries like entertainment, healthcare, and virtual assistants. This position typically requires strong programming skills, experience with machine learning frameworks, and a background in audio signal processing.

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

To thrive as a Full Time Audio Machine Learning Engineer, you need a solid background in machine learning, digital signal processing, and proficiency in programming languages like Python or C++, often supported by a degree in computer science, engineering, or a related field. Experience with frameworks such as TensorFlow or PyTorch, and familiarity with audio analysis libraries and cloud computing platforms, are typically required. Strong analytical thinking, collaboration, and problem-solving skills help you tackle complex audio data challenges and work effectively in multidisciplinary teams. These competencies are crucial for developing, optimizing, and deploying robust audio ML solutions that drive innovation in areas like speech recognition, music analysis, and sound classification.

What are some common challenges faced by professionals in full-time audio machine learning roles, and how can they be addressed?

Professionals in full-time audio machine learning roles often face challenges such as dealing with noisy or unbalanced datasets, managing high computational requirements for model training, and ensuring real-time processing capabilities. Overcoming these challenges typically involves applying advanced data augmentation techniques, leveraging specialized hardware (like GPUs), and optimizing models for efficiency. Collaboration with data engineers and domain experts is also crucial to refine data pipelines and validate model outputs. Staying updated with the latest research and open-source tools can further enhance problem-solving in this rapidly evolving field.
More about Full Time Audio Machine Learning jobs
What are the most commonly searched types of Audio Machine Learning jobs? The most popular types of Audio Machine Learning jobs are:
What job categories do people searching Full Time Audio Machine Learning jobs look for? The top searched job categories for Full Time Audio Machine Learning jobs are:
Full Time Audio Machine Learning jobs near you
Infographic showing various Full Time Audio Machine Learning job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $84,456 per year, or $40.6 per hour.
Machine Learning Researcher, Audio

Machine Learning Researcher, Audio

Bland

San Francisco, CA • On-site

$140K - $250K/yr

Full-time

Medical, Dental, Vision

Posted 20 days ago

Job description

Machine Learning Researcher, Audio
Location: San Francisco, CA or Remote
About Bland
At Bland.com, our mission is to empower enterprises to build AI phone agents at scale. Based in San Francisco, we are a fast-growing team reimagining how customers interact with businesses through voice. We have raised $65 million from leading Silicon Valley investors, including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs.
Voice is quickly becoming the primary interface between businesses and their customers. We are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human.
The Role: Machine Learning Researcher, Audio
As a Machine Learning Researcher at Bland, you'll be working on foundational research and development across the core components of our voice stack: speech-to-text, large language models, neural audio codecs, and text-to-speech. Your work will define how our agents understand, reason, and speak in real time at enterprise scale.
This is not a narrow research role. You will take ideas from theory to large-scale training to production inference systems serving millions of calls per day. You will design new modeling approaches, validate them with rigorous experimentation, and collaborate with engineering teams to deploy them into real customer environments.
What You Will Do
Build and Scale Next-Generation TTS Systems
  • Design and train large scale text-to-speech models capable of expressive, controllable, human-sounding output.
  • Develop neural audio codec-based TTS architectures for efficient, high-fidelity generation.
  • Improve prosody modeling, question inflection, emotional expression, and multi-speaker robustness.
  • Optimize for real-time, low-latency inference in production.

Advance Speech-to-Text Modeling
  • Build and fine-tune large scale ASR systems robust to accents, noise, telephony artifacts, and code switching.
  • Leverage self-supervised pretraining and large-scale weak supervision.
  • Improve transcription accuracy for real-world enterprise scenarios, including structured extraction and conversational nuance.

Pioneer Neural Audio Codecs
  • Research and implement neural audio codecs that achieve extreme compression with minimal perceptual loss.
  • Explore discrete and continuous latent representations for scalable speech modeling.
  • Design codec architectures that enable downstream generative modeling and controllable synthesis.

Develop Scalable Training Pipelines
  • Curate and process massive audio datasets across languages, speakers, and environments.
  • Design staged training curricula and data filtering strategies.
  • Scale training across distributed GPU clusters focusing on cost, throughput, and reliability.

Run Rigorous Experiments
  • Design ablation studies that isolate the impact of architectural changes.
  • Measure improvements using both objective metrics and perceptual evaluations.
  • Validate ideas quickly through focused experiments that confirm or eliminate hypotheses.

What Makes You a Great Fit
Deep Research Foundations
  • Experience with self-supervised learning, multimodal modeling, or generative modeling.
  • Ability to derive new formulations and implement them efficiently.

Expertise in Voice Modeling
  • Hands-on experience building or scaling TTS, STT, or neural audio codec systems.
  • Familiarity with large scale speech datasets and real-world audio variability.
  • Strong intuition for audio quality, prosody, and conversational dynamics.

Systems and Hardware Awareness
  • Experience training and serving large models on modern accelerators.
  • Knowledge of inference optimization techniques, including quantization, kernel optimization, and memory efficiency.
  • Understanding of real-time constraints in telephony or streaming environments.

Experimental Rigor
  • Track record of designing controlled experiments and meaningful ablations.
  • Comfortable working with both offline benchmarks and live production metrics.
  • Ability to move quickly from hypothesis to validation.

Builder Mentality
  • Comfortable in fast-moving startup environments.
  • Strong ownership mindset from research through deployment.
  • Excited by ambiguous, unsolved problems.

How You Show Up
  • You treat unsolved problems as opportunities to invent new paradigms.
  • You identify the single experiment that can validate an idea in days, not months.
  • You measure everything and let data drive decisions.
  • You are obsessed with making voice agents sound truly human.
  • You use AI tools aggressively to amplify your own impact and accelerate research cycles.

Bonus Points
  • Experience with large scale distributed training.
  • Research publications or open source contributions in speech or language AI.
  • Background in real-time speech systems or telephony.
  • PhD in ML, AI, or a related field, or equivalent research impact.

Benefits and Compensation
  • Healthcare, dental, vision, all the good stuff
  • Meaningful equity in a fast-growing company
  • Every tool you need to succeed
  • Beautiful office in Jackson Square, SF with rooftop views
  • Competitive salary: $160,000 to $250,000

If you are energized by building and scaling TTS models, pioneering neural audio codecs, and pushing the boundaries of speech-to-text systems, we would love to hear from you.

Apply