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Deep Learning Engineer Jobs in Seattle, WA (NOW HIRING)

Apple

AIML - Machine Learning Engineer, MIND

Seattle, WA · On-site

$142K - $263K/yr

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you will have ... Familiar with recent advances in deep learning. Bachelor's, Master's, or PhD or equivalent ...

Apple

Machine Learning Engineer

Seattle, WA

$175K - $308K/yr

In this position, you will be part of our extraordinary team of Computer Graphics, Computer Vision and Deep Learning researchers and engineers to discover and build solutions to previously-unsolved ...

Hive

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

We are looking for developers who are excited about staying at the forefront of deep learning technology, prototyping state-of-the-art neural net models and launching these models into production. We ...

Hive

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

We are looking for developers who are excited about staying at the forefront of deep learning technology, prototyping state-of-the-art neural net models and launching these models into production. We ...

Remitly

Staff Machine Learning Engineer

Seattle, WA · On-site

$208K - $260K/yr

As a Staff Machine Learning Engineer in Remitly's Core AI/ML team, you'll work at the heart of our ... Experience with one or more of Deep Learning algorithms, Large Language Models, Causal Inference ...

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Showing results 21-40

All JobsDeep Learning Engineer JobsDeep Learning Engineer Jobs in Seattle, WA

Deep Learning Engineer information

See Seattle, WA salary details

$43.3K

$131.9K

$218.1K

How much do deep learning engineer jobs pay per year?

As of Sep 2, 2026, the average yearly pay for deep learning engineer in Seattle, WA is $131,931.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $172,500.00 per year, depending on experience, location, and employer.

What is a deep learning engineer?

A Deep Learning Engineer is a specialized software engineer who designs, develops, and optimizes deep learning models. They work with neural networks, large datasets, and frameworks like TensorFlow or PyTorch to build AI systems for tasks like image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, performance tuning, and deploying models into production. Strong programming skills in Python, knowledge of machine learning algorithms, and experience with GPU acceleration are essential for this role.

What does a deep learning engineer do?

Deep Learning Engineers typically spend their days designing, developing, and optimizing neural network models for tasks like image recognition, natural language processing, or recommendation systems. They preprocess and analyze large datasets, experiment with model architectures, and tune hyperparameters to achieve the best performance. Collaboration is often required with data scientists, product managers, and software engineers to integrate models into real-world applications and scale solutions for production. Additionally, many deep learning engineers review current research, stay updated on advancements in AI, and continuously improve their skills. This role offers a dynamic work environment where learning and innovation are highly encouraged.

What skills and qualifications does a deep learning engineer need?

To thrive as a Deep Learning Engineer, you need a strong background in mathematics, machine learning theory, and programming (especially Python), often supported by a relevant degree in computer science, engineering, or related fields. Proficiency with frameworks such as TensorFlow, PyTorch, Keras, as well as experience with GPUs and cloud platforms, is highly valued, and certifications in AI or deep learning can further enhance your profile. Effective problem-solving, strong collaboration skills, and clear communication are important soft skills for excelling in interdisciplinary teams. These abilities ensure that you can develop robust deep learning models, adapt to evolving technologies, and contribute value in both technical and collaborative settings.

Are deep learning engineers in demand?

Deep learning engineers are in high demand due to the growth of artificial intelligence and machine learning applications across industries such as technology, healthcare, and finance. They typically require skills in neural networks, programming languages like Python, and frameworks such as TensorFlow or PyTorch, with job opportunities increasing as AI adoption expands.

What are popular job titles related to Deep Learning Engineer jobs in Seattle, WA?

For Deep Learning Engineer jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Deep Learning Engineer jobs in Seattle, WA look for?

The top searched job categories for Deep Learning Engineer jobs in Seattle, WA are:

Deep Learning Engineer jobs near you
Infographic showing various Deep Learning Engineer job openings in Seattle, WA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $131,931 per year, or $63.4 per hour.

Senior Deep Learning Systems Engineer, Datacenters

Nvidia

Redmond, WA • Hybrid

$117K - $160K/yr

Full-time

Re-posted 3 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies

Job description

As NVIDIA makes inroads into the Datacenter business, our team plays a central role in getting the most out of our exponentially growing datacenter deployments as well as establishing a data-driven approach to hardware design and system software development. The role of a Deep Learning Systems Engineer would be to analyze the performance and power consumption of deep learning applications on datacenter-class hardware and significantly influence the design and optimization of datacenters.

Do you want to influence the development of high-performance Datacenters designed for the future of AI? Do you have an interest in system architecture and performance? In this role you will find how CPU, GPU, networking, and IO relate to deep learning (DL) architectures for Natural Language Processing, Computer Vision, Autonomous Driving and other technologies. Come join our team, and bring your interests to help us optimize our next generation systems and Deep Learning Software Stack.

What you'll be doing:

  • Help develop software infrastructure to characterize and analyze a broad range Deep Learning applications

  • Evolve cost-efficient datacenter architectures tailored to meet the needs of Large Language Models (LLMs).

  • Work with experts to help develop analysis and profiling tools in Python, bash and C++ to measure key performance metrics of DL workloads running on Nvidia systems.

  • Analyze system and software characteristics of DL applications.

  • Develop analysis tools and methodologies to measure key performance metrics and to estimate potential for efficiency improvement.

What we need to see:

  • A Bachelor's degree in Electrical Engineering or Computer Science or equivalent experience (Masters or PhD degree preferred).

  • 8 years or more of relevant experience.

  • Experience in at least one of the following:

    • System Software: Operating Systems (Linux), Compilers, GPU kernels (CUDA), DL Frameworks (PyTorch, TensorFlow).

    • Silicon Architecture and Performance Modeling/Analysis: CPU, GPU, Memory or Network Architecture

  • Experience programming in C/C++ and Python. Exposure to Containerization Platforms (docker) and Datacenter Workload Managers (slurm) is a plus.

  • A deep understanding of computer system architecture and performance analysis is essential for success in this role. Applicants should have demonstrated hands-on experience in these domains.

  • Demonstrated ability to work in virtual environments, and a strong drive to own tasks from beginning to end. Prior experience with such environments will make you stand out.

Ways to stand out from the crowd:

  • Background with system software, Operating system intrinsics, GPU kernels (CUDA), or DL Frameworks (PyTorch, TensorFlow).

  • Experience with silicon performance monitoring or profiling tools (e.g. perf, gprof, nvidia-smi, dcgm).

  • In depth performance modeling experience in any one of CPU, GPU, Memory or Network Architecture

  • Exposure to Containerization Platforms (docker) and Datacenter Workload Managers (slurm).

  • Prior experience with multi-site teams or multi-functional teams.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you're creative and autonomous, we want to hear from you!

#LI-Hybrid

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 11, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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