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Audio Machine Learning Jobs in Chicago, IL (NOW HIRING)

kadence

Senior Machine Learning Engineer

Mundelein, IL

$225K - $325K/yr

Senior Machine Learning Engineer ABOUT THE ROLE This is a hands-on, high-ownership role for ML ... You'll fine-tune large language models and audio models, evaluate them with rigorous benchmarks ...

Cna

Senior Data AI Engineer

Chicago, IL · On-site

$109K - $148K/yr

... machine learning solutions that accelerate CNA's migration to a modern cloud data lakehouse. The engineer works across structured and unstructured data domains - including documents, images, audio ...

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kadence

Research Scientist

Mundelein, IL

$225K - $400K/yr

As a Founding Machine Learning Researcher, you'll focus on advancing model capabilities for human ... You'll explore new approaches across LLMs and audio models, design novel evaluation methods, and ...

Motorola

Embedded Software Engineer (DSP)

Schaumburg, IL · On-site

$63K - $97K/yr

Department Overview The team is responsible for delivering Audio and RF Signal Processing Software ... Machine Learning workflows. * Embedded Fundamentals: Experience with ARM-based or DSP ...

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All JobsAudio Machine Learning JobsAudio Machine Learning Jobs in Chicago, IL

Audio Machine Learning information

See Chicago, IL salary details

$30.4K

$87K

$176.7K

How much do audio machine learning jobs pay per year?

As of Jun 10, 2026, the average yearly pay for audio machine learning in Chicago, IL is $87,001.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,500.00 and $116,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Audio Machine Learning position, and why are they important?

To thrive in Audio Machine Learning, you need a strong background in machine learning, digital signal processing, and proficiency with programming languages such as Python or MATLAB, typically supported by a relevant degree in computer science, electrical engineering, or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with audio libraries (e.g., Librosa), and knowledge of cloud computing tools are highly valued, as are certifications in AI or data science. Strong problem-solving skills, creativity, and effective communication are essential soft skills for success in this field. These skills are crucial for developing innovative solutions, collaborating across multidisciplinary teams, and addressing complex audio data challenges in real-world projects.

What are the typical daily responsibilities of someone working in Audio Machine Learning?

Professionals in Audio Machine Learning typically spend their days designing, developing, and optimizing machine learning models tailored to audio data, such as speech or music recognition systems. You may also preprocess large datasets, extract and engineer relevant features, and collaborate closely with data scientists, audio engineers, and software developers to integrate your work into larger applications. Regular tasks often include running experiments, evaluating model performance, tuning hyperparameters, and keeping up with the latest advancements in the field. Team meetings, code reviews, and presenting findings to stakeholders are also common parts of the workweek.

What is an Audio Machine Learning job?

An Audio Machine Learning job involves developing algorithms and models that analyze, process, and generate audio data. Responsibilities typically include working with speech recognition, music analysis, sound classification, and audio enhancement. Professionals in this field use deep learning, signal processing, and neural networks to improve audio-based applications like voice assistants, noise reduction systems, and music recommendation engines. They often work with datasets of speech, music, or environmental sounds to build models that understand and manipulate audio signals effectively.

What are the most commonly searched types of Audio Machine Learning jobs in Chicago, IL? The most popular types of Audio Machine Learning jobs in Chicago, IL are:
What are popular job titles related to Audio Machine Learning jobs in Chicago, IL? For Audio Machine Learning jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Audio Machine Learning jobs in Chicago, IL look for? The top searched job categories for Audio Machine Learning jobs in Chicago, IL are:
Audio Machine Learning jobs near you
Infographic showing various Audio Machine Learning job openings in Chicago, IL as of June 2026, with employment types broken down into 75% Full Time, 13% Part Time, 4% Temporary, and 8% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $87,001 per year, or $41.8 per hour.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

kadence

Mundelein, IL

$225K - $325K/yr

Other

Medical, Dental, Vision

Posted 3 days ago

Job description

Senior Machine Learning Engineer


ABOUT THE ROLE

This is a hands-on, high-ownership role for ML engineers who want to build production models that actually ship, and perform under real-world constraints. As a Founding Senior Machine Learning Engineer, you’ll work across the ML stack to power human-like voice agents that handle millions of real-time phone conversations.


You’ll fine-tune large language models and audio models, evaluate them with rigorous benchmarks (and human feedback), and deploy them into latency-sensitive, high-traffic systems. You’ll own model performance end-to-end—from training pipelines to post-deployment monitoring—and shape our ML strategy alongside the founding team.


If you’re excited by hard technical challenges, fast iteration, and the opportunity to define how voice AI works at scale, this role is a rare chance to do it from the ground up.


KEY RESPONSIBILITIES

  • Train & Tune Models – Fine-tune LLMs and audio models to maximize speed, accuracy, and production-readiness—pushing the frontier of real-time AI voice experiences.
  • Benchmark & Evaluate – Build datasets, define rigorous metrics, and measure model performance across high-impact voice AI tasks to guide development.
  • Deploy to Production – Work closely with engineering to ship models, monitor them in the wild, and ensure they stay fast, reliable, and accurate at scale.
  • Run Human Evaluations – Build scalable pipelines to collect structured human feedback, benchmark subjective quality, and inform model iterations.
  • Level Up Infrastructure – Design and maintain the ML infrastructure needed for fast experimentation, robust training, and continuous deployment.


YOU MIGHT THRIVE IF YOU

  • ML Engineer with Real-World Experience – You’ve trained and shipped models in production. Bonus if you’ve worked with LLMs or audio models.
  • Fluent in Modern ML Stack – You know your way around Python, PyTorch, and today’s ML tools—from training pipelines to evaluation benchmarks.
  • Execution-Oriented – You move fast, take ownership, and focus on solving real problems over perfect ones.
  • Startup-Ready – You’re adaptable, resilient, and energized by ambiguity and fast-changing priorities.
  • Clear Communicator & Team Player – You collaborate well across functions and push decisions forward.


JOB DETAILS

  • Cash: $225,000 - $325,000 base salary
  • Equity: Offers Equity
  • Location: Redwood City, CA, US
  • US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.


OTHER BENEFITS

  • 100% coverage for medical, dental, and vision insurance
  • $70/day DoorDash credit for unlimited breakfast, lunch, dinner, and snacks
  • $200/month wellness reimbursement (gym, fitness classes, etc.)
  • $300/month commuter reimbursement (gas, Caltrain, etc.)
  • $75/month phone bill reimbursement
  • $50/month internet reimbursement


COMPENSATION PHILOSOPHY

  • Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed.
  • Top 1% Talent: Above-market pay (top 5 percentile) to attract high performers.
  • High Ownership: Small teams, >$1M revenue/employee, and significant equity.
  • Performance-Based: Offers tied to interview performance, not experience or past salaries.


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