1

Deep Learning Compression Jobs (NOW HIRING)

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $167K/yr

... deep learning approaches. • Expertise in model acceleration, quantization, or compression (TensorRT, ONNX Runtime). • Familiarity with real-time frameworks and middleware such as ROS 2, GStreamer ...

Design, implement, and refine deep learning models to ensure efficiency, scalability, and ... Optimize inference performance, model compression, and deployment across various hardware platforms ...

Strong classical computer vision skills (geometry-based methods, feature extraction) complementing deep learning approaches. * Expertise in model acceleration, quantization, or compression (TensorRT ...

... compression. Your work will redefine the video experience for billions of users. Description In ... D. work Proven track record of success in deep learning, with publications in top ML/CV venues ...

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

Strong classical computer vision skills (geometry-based methods, feature extraction) complementing deep learning approaches. * Expertise in model acceleration, quantization, or compression (TensorRT ...

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $167K/yr

... deep learning approaches. • Expertise in model acceleration, quantization, or compression (TensorRT, ONNX Runtime). • Familiarity with real-time frameworks and middleware such as ROS 2, GStreamer ...

... compression, and low-power operation. If you enjoy great rewards, Ambarella has it all, great ... Training and optimization of deep learning/ML based computer vision algorithm for edge devices.

Showing results 21-40

Deep Learning Compression information

See salary details

$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.

Sr. Machine Learning Research Engineer, Siri Speech

Cupertino, CA • On-site

Apple
Computer and Electronic Product Manufacturing • 10K+ employees

$252K/yr

Full-time

Re-posted 16 days ago


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 684 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

Join the team redefining what a deeply personal and integrated assistant can be.
As part of the Siri organization, you will help shape one of the world's most widely used AI assistants, powered by our next-generation of Apple Intelligence, with capabilities like personal context understanding and on-screen awareness, built with privacy from the ground up. Your work will have direct, meaningful impact for users across iOS, iPadOS, macOS, watchOS, and visionOS.
This is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design, shipping technology that is centered around users and their needs.
Description
On the Siri team, you will work alongside a fast-growing team of world-class engineers and scientists to tackle core problems in efficient machine learning for effective
dialog systems and foundation models-ranging from natural language understanding and multi-turn context tracking, to the integration of speech, text, and other modalities.
Minimum Qualifications
Demonstrated expertise in efficient deep learning with publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, KDD, ACL, ICASSP, InterSpeech) or a track record in applying efficient deep learning techniques to products
Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or Tensorflow
PhD in Mathematics or Computer Science, or other technical field, or equivalent industry experience
Preferred Qualifications
Strong expertise in efficient machine learning, model compression and algorithm optimization techniques
A track record in software design, coding and parallel computing
Experience with large scale machine learning training/evaluation
On-device intelligence and learning with strong privacy protections
Ability to work in a collaborative environment

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976