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Entry Level 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 ...

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 ...

Roku

Software Engineer, Machine Learning

San Jose, CA · On-site

$135K - $185K/yr

You will explore Picture Quality (PQ) and Audio Quality (AQ) improvements using AI in a resource ... Hands-on experience with Machine Learning / Deep Learning frameworks like TensorFlow or PyTorch

Apple

Senior Machine Learning Engineer

Seattle, WA

$171K - $258K/yr

Research and develop advanced machine learning solutions for natural language processing (NLP), audio, computer vision, and multi-modal applications. Work closely with crossfunctional partners ...

Hive

Machine Learning Engineer

Seattle, WA

$120K - $180K/yr

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

Hive

Machine Learning Engineer

San Francisco, CA · On-site

$120K - $180K/yr

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

Hive

Machine Learning Engineer

San Francisco, CA

$120K - $180K/yr

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

Hive

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

You have successfully trained and deployed a deep learning machine model (image, NLP, video, or audio) into production, with measurably improved performance over baseline, either in industry or as a ...

NxT Level

Senior Machine Learning Engineer

Manhattan, NY · On-site

$133K - $176K/yr

If you're excited about the future of video, audio, video content moderation, and more then you ... A degree in Computer Science, Machine Learning, or a related field, or equivalent professional ...

NxT Level

Senior Machine Learning Engineer

Manhattan, NY · On-site

$133K - $176K/yr

If you're excited about the future of video, audio, video content moderation, and more then you ... A degree in Computer Science, Machine Learning, or a related field, or equivalent professional ...

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Entry Level Audio Machine Learning information

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How much do entry level audio machine learning jobs pay per hour?

As of Jul 9, 2026, the average hourly pay for entry level audio machine learning in the United States is $17.46, according to ZipRecruiter salary data. Most workers in this role earn between $15.62 and $18.99 per hour, depending on experience, location, and employer.

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

AspectEntry Level Audio Machine LearningEntry Level Audio Signal Processing
Required CredentialsBachelor's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Acoustics, or related; knowledge of DSP tools
Work EnvironmentTech companies, research labs, startups focusing on AI applications in audioAudio equipment manufacturers, telecommunications, research labs
Industry UsageDeveloping AI models for speech recognition, audio classification, sound analysisDesigning audio filters, signal enhancement, audio hardware integration

Entry Level Audio Machine Learning focuses on developing AI models for audio applications, requiring programming and ML skills. Entry Level Audio Signal Processing emphasizes traditional audio analysis and hardware integration, requiring DSP knowledge. Both roles are essential in the audio industry but differ in technical focus and tools used.

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 Entry Level Audio Machine Learning jobs look for? The top searched job categories for Entry Level Audio Machine Learning jobs are:
Entry Level Audio Machine Learning jobs near you
Machine Learning Researcher, Audio

Machine Learning Researcher, Audio

Bland

San Francisco, CA • On-site

$140K - $250K/yr

Full-time

Medical, Dental, Vision

Re-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.

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