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

Adobe

Sr. Machine Learning Engineer 4

Seattle, WA

$118K - $163K/yr

Develop, evaluate, and deploy ML models using classical, deep learning, and GenAI approaches ... Mentor junior engineers and help grow the team's technical depth. What You Need to Succeed Required ...

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 ...

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Deep Learning Engineer information

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$43.2K

$131.9K

$217.9K

How much do deep learning engineer jobs pay per year?

As of Jul 5, 2026, the average yearly pay for deep learning engineer in Seattle, WA is $131,857.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $172,400.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior Deep Learning Engineer or AI research director, often involving advanced skills in machine learning frameworks, data modeling, and large-scale system development. These roles usually require extensive experience, specialized knowledge, and may include leadership responsibilities or working in cutting-edge AI research environments.

What is a Deep Learning Engineer job?

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 are the key skills and qualifications needed to thrive in the Deep Learning Engineer position, and why are they important?

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.

What engineers make $500,000?

Senior engineers in high-demand fields such as software, data science, and machine learning can earn $500,000 or more annually, especially with extensive experience, specialized skills, and leadership roles. Roles like senior software engineers, machine learning engineers, and data architects at large tech companies or startups often reach this compensation level through base salary, bonuses, and stock options.

What do deep learning engineers do?

Deep learning engineers develop and implement neural network models to solve complex problems such as image recognition, natural language processing, and speech analysis. They work with large datasets, use frameworks like TensorFlow or PyTorch, and often require knowledge of programming, mathematics, and machine learning principles.

What are the typical daily tasks and responsibilities of a Deep Learning Engineer?

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 engineers make $300,000 a year?

Senior deep learning engineers and AI specialists with extensive experience, advanced skills in machine learning frameworks, and strong domain knowledge can earn $300,000 or more annually. These roles often require advanced degrees, certifications, and work in high-demand industries such as technology, finance, or healthcare, typically involving leadership responsibilities and complex project management.
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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
Senior Deep Learning Systems Engineer, Datacenters

Senior Deep Learning Systems Engineer, Datacenters

Nvidia

Redmond, WA • Hybrid

$117K - $160K/yr

Full-time

Posted 4 days ago

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.
Nvidia logo

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

Year founded

1993

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