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Freelance 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 ...

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 ...

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 ...

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 ...

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 ...

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

Hive

Machine Learning Manager

San Francisco, CA

$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

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 ...

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All JobsFreelance Audio Machine Learning Jobs

Freelance Audio Machine Learning information

See salary details

$64K

$70K

$74.5K

How much do freelance audio machine learning jobs pay per year?

As of Jun 26, 2026, the average yearly pay for freelance audio machine learning in the United States is $70,000.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,500.00 and $72,500.00 per year, depending on experience, location, and employer.

What is the difference between Freelance Audio Machine Learning vs Freelance Audio Signal Processing?

AspectFreelance Audio Machine LearningFreelance Audio Signal Processing
Required SkillsMachine learning, programming, audio analysisDigital signal processing, audio engineering, software tools
Work EnvironmentData analysis, model development, algorithm designAudio editing, filtering, signal enhancement
Common EmployersTech companies, startups, research labsAudio production houses, broadcasting, music industry
CertificationsMachine learning, data science, programming certificationsAudio engineering, DSP certifications

Freelance Audio Machine Learning focuses on developing algorithms and models to analyze and interpret audio data using machine learning techniques. In contrast, Freelance Audio Signal Processing involves applying traditional digital signal processing methods to enhance or modify audio signals. Both roles require technical expertise but differ in their core methods and applications.

More about Freelance Audio Machine Learning jobs
What cities are hiring for Freelance Audio Machine Learning jobs? Cities with the most Freelance Audio Machine Learning job openings:
What are the most commonly searched types of Audio Machine Learning jobs? The most popular types of Audio Machine Learning jobs are:
What states have the most Freelance Audio Machine Learning jobs? States with the most job openings for Freelance Audio Machine Learning jobs include:
What job categories do people searching Freelance Audio Machine Learning jobs look for? The top searched job categories for Freelance Audio Machine Learning jobs are:
Freelance Audio Machine Learning jobs near you
Infographic showing various Freelance Audio Machine Learning job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $70,000 per year, or $33.7 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 8 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