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Deep Learning Scientist Jobs in California (NOW HIRING)

Skydio, Inc

Autonomy Engineer - Deep Learning

San Mateo, CA · On-site

$170K - $277K/yr

D. in computer science, electrical engineering or related discipline * Demonstrated hands-on experience designing, training and deploying deep learning models * Ability to deliver high quality, well ...

Relace

Machine Learning Scientist

San Francisco, CA · On-site

Our team is made up of mathematicians, physicists, and computer scientists who are deeply ... Strong background in machine learning, deep learning, or related fields. * 2+ years of experience ...

Hayden AI

Staff Deep Learning Engineer

San Francisco, CA · On-site

$230K - $299K/yr

About the Role As a Staff Deep Learning Engineer in the Deep Learning team at Hayden, you are a ... Bachelor's degree in Computer Science, Robotics, Computer Vision, Electrical Engineering, or a ...

Skydio

Senior Autonomy Engineer - Deep Learning

San Mateo, CA · On-site

$63 - $81.25/hr

The role involves designing and implementing deep learning solutions for real-time object detection ... scientific papers and literature in computer vision • Ability to thrive in a fast paced ...

Matroid

Deep Learning Field Engineer

Palo Alto, CA · On-site

They are seeking a Deep Learning Field Engineer to develop and deploy cutting-edge CV systems that ... Qualifications : Required : • Bachelor's degree in computer science, computer engineering ...

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All JobsDeep Learning Scientist JobsDeep Learning Scientist Jobs in California

Deep Learning Scientist information

See California salary details

$37K

$121.1K

$193.9K

How much do deep learning scientist jobs pay per year?

As of Aug 10, 2026, the average yearly pay for deep learning scientist in California is $121,131.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,200.00 and $134,200.00 per year, depending on experience, location, and employer.

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

To thrive as a Deep Learning Scientist, you need a solid background in machine learning, statistics, and programming, often supported by an advanced degree in computer science or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience with cloud computing platforms, and proficiency in Python are typically required. Strong problem-solving skills, creativity, and the ability to communicate complex ideas clearly set outstanding candidates apart. These capabilities are essential for developing innovative AI solutions, interpreting results, and collaborating effectively in multidisciplinary teams.

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

AspectDeep Learning ScientistMachine Learning Engineer
Required CredentialsMaster's or PhD in Computer Science, Data Science, or related fields; strong background in deep learning frameworksBachelor's or Master's in Computer Science or related fields; proficiency in machine learning algorithms and software engineering
Work EnvironmentResearch-focused, experimental, often in R&D teamsDevelopment and deployment-focused, working on production systems
Employer & Industry UsageTech companies, research labs, AI startupsTech firms, finance, healthcare, and industries deploying ML models

While both roles involve machine learning, Deep Learning Scientists focus on developing advanced neural network models and research, whereas Machine Learning Engineers implement, optimize, and deploy these models in real-world applications.

What is a deep learning scientist?

Deep Learning Scientists are experts who design, develop, and implement advanced machine learning models inspired by the structure and function of the brain, known as artificial neural networks. They work with large datasets to train algorithms that can recognize patterns, make predictions, and solve complex problems in areas such as image recognition, natural language processing, and autonomous systems. Deep Learning Scientists often collaborate with software engineers, data scientists, and domain specialists to deploy models in real-world applications like healthcare, finance, and self-driving cars.

What are some typical challenges faced when working as a deep learning scientist, and how can they be addressed?

Deep Learning Scientists often encounter challenges such as managing large datasets, tuning complex model architectures, and ensuring reproducibility of experiments. Handling these issues requires strong skills in data preprocessing, familiarity with version control systems, and experience with frameworks like TensorFlow or PyTorch. Collaborating closely with cross-functional teams—including data engineers, software developers, and domain experts—can also help in overcoming technical and project-related obstacles. Continuous learning and staying updated with the latest research is essential to excel in this rapidly evolving field.
What are popular job titles related to Deep Learning Scientist jobs in California? For Deep Learning Scientist jobs in California, the most frequently searched job titles are:
What job categories do people searching Deep Learning Scientist jobs in California look for? The top searched job categories for Deep Learning Scientist jobs in California are:
Deep Learning Scientist jobs near you
Infographic showing various Deep Learning Scientist job openings in California as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $121,131 per year, or $58.2 per hour.

Senior Deep Learning Performance Architect

Nvidia

Santa Clara, CA • On-site

$196K/yr

Full-time

Re-posted 28 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

We are now seeking a Senior Deep Learning Performance Architect! NVIDIA is looking for outstanding Performance Architects with a background in performance analysis, performance modeling, and AI/deep learning to help analyze and develop the next generation of architectures that accelerate AI and high-performance computing applications.

What you'll be doing:

  • Develop innovative architectures to extend the state of the art in deep learning performance and efficiency

  • Analyze performance, cost and power trade-offs by developing analytical models, simulators and test suites

  • Understand and analyze the interplay of hardware and software architectures on future algorithms, programming models and applications

  • Evaluate PPA (performance, power, area) for hardware features and system level architectural trade-offs. Develop high level simulators in C++/Python

  • Actively collaborate with software, product and research teams to guide the direction of deep learning HW and SW

What we need to see:

  • MS or PhD in Computer Science, Computer Engineering, Electrical Engineering or equivalent experience

  • 6+ years of relevant meaningful work experience

  • Strong background in GPU or Deep Learning ASIC architecture for distributed training and/or inference spanning multi-chip/multi-node

  • Experience with performance modeling, architecture simulation, profiling, and analysis

  • Solid foundation in machine learning and deep learning. Understanding of modern transformer-based architectures and their performance at scale.

  • Strong programming skills in Python, C, C++

Ways to stand out from the crowd:

  • Background with deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, JAX, TensorRT)

  • Familiarity with advanced optimizations and SW/HW co-design in LLM training and inference

  • Exposure to using AI to accelerate SW engineering

  • Demonstration of self-motivation and creative / critical thinking

Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. NVIDIA's GPUs run AI algorithms, simulating human intelligence, and act as the brains of computers, robots and self-driving cars that can perceive and understand the world. Increasingly known as "the AI computing company", NVIDIA wants you! Come, join our Deep Learning Architecture team, where you can help build real-time, efficient computing platforms driving our success in this exciting and rapidly growing field.

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 June 7, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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

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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