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Music Ai Audio Python Jobs (NOW HIRING)

Adobe, Inc.

Senior Research Scientist

San Francisco, CA · On-site

$116K - $147K/yr

The Opportunity The Speech AI Lab at Adobe Research is seeking a Senior Audio Research Scientist to ... Strong Python and deep learning development skills, with attention to performance, reproducibility ...

Dolby

Senior Generative AI Researcher

Atlanta, GA · On-site

AI driven audio enhancement, processing, and generation (for speech and music), such as speech ... Highly skilled in Python and one or more popular deep learning frameworks (TensorFlow or PyTorch)

Besimple AI

Strategic Projects Lead -- Audio Data

$53 - $71.75/hr

They are seeking a Strategic Projects Lead -- Audio Data to manage high-priority audio data ... Python, Airtable, Retool, no-code automation, or workflow tooling experience. Company : besimple AI ...

Sirius XM

Staff Software Engineer, Python

Oakland, CA · On-site

$106K - $200K/yr

Our critically-acclaimed, industry-leading audio entertainment encompasses music, sports, comedy ... Expertise in Python (Django, FastAPI, or similar frameworks) and backend architecture (e.g ...

Hume AI

AI Researcher

San Jose, CA · On-site +1

$180K - $350K/yr

... of audio and video recordings, making SLMs superhumanly helpful, interesting, funny, eloquent ... Expertise in the Python ecosystem and popular ML libraries and tools (e.g. PyTorch) * Experience ...

Perle AI

Expert Audio Transcriber, Hebrew (US, Canada, EU)

$20.50 - $26.50/hr

ABOUT PERLE Perle is an AI infrastructure company building expert-driven training data, evaluation ... and music * Make principled orthographic decisions for dialectal, colloquial, and code-switched ...

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Music Ai Audio Python information

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$23K

$140K

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How much do music ai audio python jobs pay per year?

As of Jul 14, 2026, the average yearly pay for music ai audio python in the United States is $139,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,500.00 and $164,500.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Music AI Audio Python developers when integrating AI models into digital audio workstations (DAWs)?

Music AI Audio Python developers often encounter challenges when integrating AI models into DAWs, such as ensuring low-latency audio processing and maintaining compatibility with various audio formats and plugin standards. Additionally, developers must optimize AI models for real-time performance while balancing computational efficiency. Collaboration with audio engineers and musicians is common, as their feedback helps refine features and improve usability. Staying updated with the latest advancements in both AI and audio technology is also crucial for success in this dynamic role.

What are the key skills and qualifications needed to thrive as a Music AI Audio Python Engineer, and why are they important?

To thrive as a Music AI Audio Python Engineer, you need a strong background in computer science, digital signal processing, and music theory, typically supported by a degree in a related field. Proficiency in Python, machine learning frameworks like TensorFlow or PyTorch, and audio libraries such as librosa is essential. Creative problem-solving, strong communication, and adaptability help you collaborate with interdisciplinary teams and innovate in music technology. These skills enable the development of advanced AI-driven music applications that meet both technical and artistic requirements.

What is the difference between Music Ai Audio Python vs Music Producer?

AspectMusic Ai Audio PythonMusic Producer
Required SkillsProgramming, AI, audio processingMusic theory, production, sound engineering
Work EnvironmentTech companies, studios, research labsRecording studios, production houses, live events
CertificationsPython, AI, audio engineering certificationsMusic production, sound engineering certifications
Industry UsageDeveloping AI tools for music creation and analysisCreating, mixing, and producing music tracks

Music Ai Audio Python focuses on developing AI-driven audio applications using Python, requiring programming and technical skills. In contrast, a Music Producer is involved in the creative and technical process of making music, emphasizing musical skills and industry experience. Both roles are essential in the music industry but serve different functions and skill sets.

What is a Music AI Audio Python developer?

A Music AI Audio Python developer is a software engineer or data scientist who specializes in creating tools, models, or applications that use artificial intelligence to analyze, generate, or manipulate music and audio data, primarily using the Python programming language. They work with machine learning libraries and audio processing frameworks to build applications such as music recommendation systems, audio classification tools, or AI-based music composition software. These professionals combine knowledge of music theory, digital signal processing, and AI to develop innovative solutions in the audio technology space.
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Music Ai Audio Python jobs near you
Infographic showing various Music Ai Audio Python job openings in the United States as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $139,971 per year, or $67.3 per hour.
Audio Inference Engineer, Model Efficiency

Audio Inference Engineer, Model Efficiency

Cohere

New York, NY • On-site

Full-time

Medical, Dental, Retirement, PTO

Posted 8 days ago

Job description

Who are we?
Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.
We're training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.
We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.
We are a global technology company co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us!
Why this role?
Our team is a fast-growing group of committed researchers and engineers. The mission of the team is to build reliable machine learning systems and optimize audio inference serving efficiency using innovative techniques. As an engineer on this team, you will work on advancing core audio model serving metrics, including latency, throughput, and quality by diving deep into our systems, identifying bottlenecks, and delivering creative solutions for audio processing and streaming workloads.
You'll collaborate closely with both the training and serving infrastructure teams to ensure seamless integration between model development and deployment, with a special focus on real-time and streaming audio inference.
Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations.
You may be a good fit for the team if you have:
  • Significant experience developing high-performance audio or machine learning inference systems.
  • Proficiency with programming languages such as C++ and Python.
  • Hands-on experience with deep learning models for audio, speech, or language applications.
  • A bias for action and a strong results-oriented mindset.
It is a big plus if you also have considerable experience with:
  • GPU programming, low-level system optimization, model parallelization techniques over multiple GPUs
  • Have experience with duplex real-time streaming architectures.
  • Internals of machine learning frameworks for audio (such as PyTorch, TensorFlow, or specialized audio libraries).
  • Have experience with inference framework like vLLM, SGLang, Tensort-LLM, or custom distributed inference systems
  • Sequence modeling (e.g., transformers for audio/speech) and end-to-end audio pipeline optimization

Full-Time Employees at Cohere enjoy these Perks:
  • A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
  • Full health and dental benefits, including a separate budget for mental health.
  • RRSP matching, 401K, Pension Scheme.
  • 100% Parental Leave top-up for up to 6 months, for either parent.
  • Annual enrichment benefits:
    Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
    Education & learning stipend for conferences, courses, and coaching.
  • 6 weeks of paid vacation (30 working days!)
  • Budget for traveling to other offices if you are remote, plus an annual company offsite.

How and Where We Work:
  • Cohere is remote-friendly. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming soon.
  • For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
  • For those not near an office: a co-working benefit so you can work alongside others in your city.
  • Everyone receives a $500 home office stipend to set up your workspace properly.

If any of the above doesn't line up exactly with your experience, we still encourage you to apply.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.
We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.

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