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Deep Learning Developer Jobs in California (NOW HIRING)

We're looking for a Deep Learning Field Engineer to operate at the forefront of CV deployment in industry - building best-in-class CV systems that leverage deep learning techniques to solve a broad ...

We're looking for a Deep Learning Field Engineer to operate at the forefront of CV deployment in industry - building best-in-class CV systems that leverage deep learning techniques to solve a broad ...

We're looking for a Deep Learning Field Engineer to operate at the forefront of CV deployment in industry - building best-in-class CV systems that leverage deep learning techniques to solve a broad ...

They are seeking a Deep Learning Field Engineer to train and deploy CV models, integrate systems, and empower customers with CV solutions. Responsibilities : • Train state-of-the-art CV models ...

Senior Autonomy Engineer - Deep Learning

San Mateo, CA · On-site

$63 - $81.25/hr

Required : • Demonstrated hands-on experience creating and deploying deep learning models • Experience curating synthetic and real-world image datasets • Solid software engineering foundation ...

Showing results 21-40

Deep Learning Developer information

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

To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.

What is a deep learning developer?

Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.

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

AspectDeep Learning DeveloperMachine Learning Engineer
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with neural networksBachelor's or Master's in CS, Data Science, or related; knowledge of algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on neural networksData-driven companies, software firms, industries applying machine learning
Industry UsagePrimarily in AI research, neural network development, deep learning projectsBroader application including predictive modeling, data analysis, and ML systems

Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.

What are some common challenges deep learning developers face when deploying models to production environments?

Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.
What cities in California are hiring for Deep Learning Developer jobs? Cities in California with the most Deep Learning Developer job openings:

Manager, Deep Learning - Autonomous Vehicles and Robotics

Nvidia

Santa Clara, CA

Full-time

Re-posted 19 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

Join our Deep Learning Engineering team within NVIDIA's Tegra Solutions Engineering organization, where we deliver production-quality deep learning solutions for autonomous vehicles and robotics on edge hardware. As a key member of our team, you'll lead a group of highly skilled engineers. We work at the intersection of modern model architectures, compiler technology, and embedded deployment. Application areas include end-to-end autonomous driving, vision-language-action models, multi-camera perception, and robotic foundation models. You'll define and drive strategic technical initiatives, working directly with automotive OEMs and robotics partners to solve their toughest optimization challenges on NVIDIA DRIVE and Jetson platforms. You'll coordinate extensively with NVIDIA Research, hardware, and compiler teams to advance the state-of-the-art in deep learning for physical AI!

What you'll be doing:

  • Lead and develop a team of deep learning engineers delivering inference optimization and model enablement solutions for automotive and robotics customers.

  • Drive end-to-end technical engagements with OEM partners, owning scoping, resource allocation, and delivery of production-quality solutions.

  • Set technical direction on how modern architectures (transformers, vision-language models, state space models) are optimized and deployed on GPU and SOC platforms.

  • Partner with compiler, runtime, and hardware teams to connect customer workload patterns with platform capabilities and roadmap priorities.

  • Collaborate with NVIDIA Research and internal deep learning teams to bring brand new techniques into production!

  • Represent NVIDIA externally at partner reviews, conferences, and industry forums.

What we need to see:

  • Master's degree or equivalent experience in Computer Science, Electrical Engineering, or a related field.

  • 8+ years of overall experience with at least 5 years in deep learning model optimization, inference engineering, or neural network compilation.

  • 4+ years of team leadership experience

  • Proven ability to manage concurrent technical customer engagements and deliver under production constraints.

  • Strong knowledge of current DL architectures and inference optimization toolchains (TensorRT or equivalent).

  • Excellent communication skills with the ability to engage credibly with both OEM engineering leadership and deep technical ICs.

Ways to stand out from the crowd:

  • Experience leading DL optimization teams in the autonomous vehicle or robotics domain with direct OEM or Tier-1 engagement.

  • Background in training pipeline optimization, curriculum design, or end-to-end autonomous driving architectures.

  • Experience with ML compiler frameworks (TVM, MLIR, XLA, Triton) or inference runtime development.

  • Familiarity with automotive safety standards (ISO 26262, SOTIF) and their implications for inference system design.

  • Track record of building engineering teams in growing competitive talent markets and experience with Agentic AI frameworks, tools, and protocols like LangChain, LangGraph, MCP or equivalent experience

The Deep Learning Engineering team within Tegra Solutions Engineering sits at the intersection of NVIDIA's most advanced AI technology and the customers. We work end-to-end: from architecture decisions with OEM engineering leadership, through optimization and deployment on DRIVE and Jetson platforms, to production vehicles and robots operating in the field. Our engineers engage directly with the world's leading automotive and robotics companies, solving problems that span next-generation network architectures, training infrastructure, inference optimization, and closed-loop simulation. We collaborate closely with NVIDIA Research, various NVIDIA AI teams, and hardware teams. The team is growing, and we are investing in building out our presence across multiple sites. If you want your work to ship on real autonomous systems and shape the platforms they run on, this is the team to join.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 3, and 272,000 USD - 431,250 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until April 26, 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


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