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Deep Learning Compression Jobs (NOW HIRING)

Leverage innovative model compression techniques to optimize performance-per-joule on custom silicon. * Be a core contributor to our deep learning and DSP development platform, advancing novel ...

Senior Deep Learning Engineer

Austin, TX · On-site +1

$130K - $180K/yr

We're hiring 3 Senior Deep Learning Engineers to join our Neural Networks team. Your primary focus ... Familiarity with model compression techniques like quantization, pruning, etc. These are permanent ...

$150 - $200/hr

Design deep learning and GenAI models to meet modeling requirements. * Implement modeling ... compression techniques (e.g., distillation). * Optimize DL or GenAI model throughput and cost ...

Machine Learning Scientist

Irvine, CA · On-site

$150 - $200/hr

Expert with PyTorch and training and testing deep learning models* Familiarity with dataset ... Familiarity with image/video compression algorithms* Computer Vision/Machine Learning publications ...

Design and develop novel machine learning algorithms and neural network architectures for video processing, compression, understanding, and enhancement. * Train, evaluate, and iterate on deep ...

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Deep Learning Compression information

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$11K

$83.9K

$140K

How much do deep learning compression jobs pay per year?

As of Sep 9, 2026, the average yearly pay for deep learning compression in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

What is deep learning compression?

Deep learning compression refers to techniques used to reduce the size, memory footprint, and computational requirements of deep neural networks without significantly sacrificing their performance. This is important for deploying models on resource-constrained devices such as smartphones or embedded systems. Common methods include pruning, quantization, knowledge distillation, and low-rank factorization. These approaches help make deep learning models more efficient and practical for real-world applications.

What are the key skills and qualifications needed to thrive as a deep learning compression engineer?

To thrive as a Deep Learning Compression Engineer, you need a strong background in deep learning, machine learning, and mathematics, typically supported by a degree in computer science or a related field. Proficiency with frameworks like TensorFlow or PyTorch, experience with model compression techniques (such as pruning, quantization, and knowledge distillation), and familiarity with hardware accelerators are essential. Strong problem-solving skills, attention to detail, and effective communication help you innovate and collaborate with research and engineering teams. These skills are critical for developing efficient AI models that meet performance and resource constraints in real-world applications.

What are the typical challenges faced when working on deep learning compression projects?

Professionals in deep learning compression often encounter challenges balancing model size reduction with maintaining high accuracy. Adapting compression techniques—such as pruning, quantization, or knowledge distillation—to different architectures and datasets requires both strong technical knowledge and experimentation. Collaboration with data scientists and software engineers is common, as solutions must be integrated into production systems without sacrificing performance. Staying up to date with rapid advances in compression research is also essential to remain effective and innovative in this role.

What is the difference between Deep Learning Compression vs Machine Learning Engineer?

AspectDeep Learning CompressionMachine Learning Engineer
Required CredentialsBachelor's or Master's in Computer Science, AI, or related fields; knowledge of neural networksBachelor's or Master's in Computer Science, AI, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, tech companies focusing on model optimizationSoftware development teams, AI startups, tech firms building ML applications
Industry UsageAI model deployment, edge computing, mobile AI applicationsDeveloping ML models, data analysis, AI product development

Deep Learning Compression focuses on reducing model size and improving efficiency of neural networks, often for deployment on limited hardware. Machine Learning Engineers develop, train, and optimize ML models across various applications. While both roles require knowledge of AI and neural networks, Deep Learning Compression specializes in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

What other helpful pages are available for Deep Learning Compression?

Other pages related to Deep Learning Compression:

Infographic showing various Deep Learning Compression job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Audio Deep Learning Engineer

San Bruno, CA • On-site

Full-time

Medical, Dental, Vision, Retirement

Re-posted yesterday


Job description


About the Role
As a Deep Learning Engineer, you will:
  • Design, develop, and deploy deep-learning-based and classical DSP audio algorithms for our SPU platform.
  • Leverage innovative model compression techniques to optimize performance-per-joule on custom silicon.
  • Be a core contributor to our deep learning and DSP development platform, advancing novel algorithms for audio processing.
  • Collaborate directly with customers across diverse industries to deliver impactful solutions and exciting new features.

This is a unique opportunity to work on challenging problems at the intersection of deep learning, model optimization, and embedded systems, while driving real-world impact in industries ranging from consumer electronics to automotive and beyond.
What You'll Do
  • Develop and optimize deep learning models for audio processing, including tasks like speech enhancement, beamforming, event detection, sound localization, voice identification, voice interfaces, noise reduction, echo cancellation, feedback cancellation, and more.
  • Drive innovation in model efficiency, compression, and deployment on embedded platforms.
  • Leverage multi-sensor data to improve algorithm performance in difficult environments.
  • Work closely with customers to understand their needs and tailor solutions to meet their goals.
  • Contribute to the end-to-end process of model development, from research and prototyping to deployment on hardware.

Requirements
  • 3+ years of relevant experience in deep learning and/or DSP engineering.
  • Strong experience with Python and PyTorch (or other deep learning frameworks).
  • A background in Computer Science, Mathematics, Electrical Engineering or a related field (BS, MS, PhD, or equivalent work experience).
  • Experience in hybrid AI / DSP algorithm development is a plus but not required.
  • A passion for solving challenging problems and collaborating in a fast-paced, innovative environment.

Desired Skills and Experience
Deep learning, Machine learning, DSP, Python, PyTorch
Benefits
  • 401(k)
  • Medical insurance
  • Vision insurance
  • Dental insurance
  • Commuter benefits
  • Disability insurance
  • Paid maternity leave
  • Paid paternity leave
  • Child care support

femtoAI is an equal opportunity employer committed to a diverse workforce which strives to create an inclusive working environment empowering everyone to do their best work. We do not discriminate on the basis of race, ethnicity, religion, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status.