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Deep Learning Ai Jobs in California (NOW HIRING)

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Deep Learning Ai information

What is a deep learning AI professional?

Deep Learning AI professionals are experts who design, develop, and implement artificial intelligence systems that use deep neural networks to analyze complex data and solve tasks such as image recognition, natural language processing, and autonomous decision-making. They work with large datasets and advanced algorithms to build models that can learn and improve over time. These professionals often have a background in computer science, mathematics, or engineering, and are skilled in programming languages like Python and frameworks such as TensorFlow or PyTorch.

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

To thrive as a Deep Learning AI Engineer, you need a strong background in mathematics, programming (especially Python), and experience with neural networks, typically supported by a degree in computer science, engineering, or a related field. Proficiency with deep learning frameworks such as TensorFlow or PyTorch, and knowledge of tools like CUDA for GPU acceleration, are essential; relevant certifications can be advantageous. Analytical thinking, creativity, and effective communication are important soft skills for solving complex problems and collaborating with cross-functional teams. These skills and qualities are crucial for building robust AI models and driving innovation in this rapidly evolving field.

What are some common challenges faced by professionals working in deep learning AI, and how can they be addressed?

Professionals in Deep Learning AI often encounter challenges such as managing large datasets, ensuring model accuracy, and addressing issues like overfitting. Collaboration with data engineers and domain experts is crucial to ensure high-quality data and relevant feature selection. Additionally, staying up-to-date with rapidly evolving frameworks and algorithms requires continuous learning and participation in knowledge-sharing within the team. Regular code reviews and experimentation with different architectures can help overcome technical obstacles and improve model performance.

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

AspectDeep Learning AiMachine Learning Engineer
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of neural networksDegree in Computer Science, Data Science, or related fields; programming skills in Python, R
Work EnvironmentResearch labs, AI development teams, tech companies focusing on AI modelsSoftware development teams, data analysis projects across various industries
Industry UsagePrimarily in AI research, autonomous systems, NLP, computer visionAcross industries for predictive modeling, data analysis, automation

Deep Learning Ai specialists focus on designing and implementing neural network models for complex AI tasks, often requiring advanced knowledge of deep neural networks. Machine Learning Engineers develop broader machine learning models, including traditional algorithms. While both roles require similar educational backgrounds, Deep Learning Ai roles are more specialized in neural networks and AI research, whereas Machine Learning Engineers work across a wider range of algorithms and applications.

What cities in California are hiring for Deep Learning Ai jobs?

Cities in California with the most Deep Learning Ai job openings:

Infographic showing various Deep Learning Ai job openings in California as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Senior Performance Engineer - Deep Learning

NVIDIA Gruppe

Santa Clara, CA • On-site

$152 - $241.50/hr

Other

Posted 3 days ago

New


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

Our Deep Learning models performance engineering team at NVIDIA is hiring software engineers at all experience levels to build and optimize the libraries and tools that enable Deep Learning Researchers and Engineers to design, develop, and deploy efficient AI applications. We are an ambitious and diverse team that builds optimizations directly into mainstream open source Deep Learning frameworks – PyTorch and JAX – which boost the performance at all levels of NVIDIA's AI stack. Our team has a wide collaborative footprint, working not only with multiple teams across NVIDIA but also with the broader open‑source community to deliver SOTA Deep Learning performance on the best AI platform in the world!

What you will be doing:
  • Build and support Transformer Engine, the open‑source library for accelerating the training of Large Language Models.
  • Collaborate on systems research that improves Deep Learning model performance, such as training using extremely low precision, parallelism methods, etc.
  • Implement, benchmark, and optimize new Deep Learning models such as LLMs straight out of groundbreaking research to scale efficiently on NVIDIA GPUs and systems.
  • Build and contribute to NVIDIA submissions on community benchmarks such as MLPerf.
  • Engage with the open‑source community as well as support enterprise customers and partners by delivering the benefits of NVIDIA’s latest hardware and software innovations.
  • Influence the design of new hardware generations and core platform software components for NVIDIA hardware and systems.
What we need to see:
  • BS or equivalent experience in Computer Science, Electrical Engineering, or a related field.
  • 3+ years of experience in C++ and Python programming.
  • Strong background, experience, or coursework in parallel systems programming, preferably on GPUs.
  • Knowledge of Computer Architecture, Code Optimization, and/or Operating Systems.
  • Proven experience in developing large software projects.
  • Excellent verbal and written communication skills.
Ways to stand out from the crowd:
  • Experience in PyTorch, JAX, or any other DL framework.
  • Experience with performance analysis, profiling, and code optimization techniques, especially with multi‑GPU or multi‑node systems.
  • Knowledge of modern LLM architectures, attention mechanisms, and/or low‑level DL libraries such as cuBLAS, cuDNN, and cuSOLVER.
  • Experience in writing GPU kernels using any of - CUDA, OpenAI Triton, CuTeDSL, Pallas, or other similar libraries.
  • Any past contributions to the open source community and/or experience working with multidisciplinary teams also showcase readiness for the team's responsibilities.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary ranges are 152,000USD – 241,500USD for Level3, and 184,000USD – 287,500USD for Level4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March8,2026.

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 is proud to be an equal opportunity employer. We do not discriminate 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.

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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