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Assistant Cuda Jobs in California (NOW HIRING)

Zoox

Software Engineer - C++ GPU Performance

San Diego, CA · On-site

$163K - $251K/yr

Strong knowledge of CUDA as applied to recent GPU microarchitectures (e.g., Ampere, Blackwell) and ... These tools assist our recruitment team but do not replace human judgment. Final hiring decisions ...

ChipAgents

ML Systems Engineer

San Jose, CA · On-site

$350K/yr

Our platform leverages cutting-edge generative AI to assist engineers in RTL design, simulation ... Strong proficiency in Python and C++/CUDA; hands-on experience with SGLang, vLLM, PyTorch, or ...

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Assistant Cuda information

What are the roles and responsibilities of an assistant CUDA developer?

An Assistant Cuda typically supports senior CUDA (Compute Unified Device Architecture) developers or teams working with NVIDIA’s parallel computing platform. Their responsibilities include assisting in developing, testing, and optimizing code written for GPUs to accelerate computing tasks, debugging CUDA applications, and maintaining documentation. They may also handle routine tasks such as performance benchmarking, code reviews, and collaborating with other team members to implement efficient GPU solutions. This role is crucial in organizations that rely on high-performance computing, scientific simulations, or AI workloads.

What skills and qualifications are needed to thrive as an assistant CUDA developer?

To thrive as an Assistant CUDA Developer, you need strong programming skills in C/C++, a solid understanding of parallel computing concepts, and familiarity with GPU architectures, often backed by a degree in computer science or a related field. Proficiency with CUDA development tools, debugging utilities, and version control systems like Git is typically required. Attention to detail, problem-solving abilities, and effective communication are crucial soft skills for collaborating with teams and optimizing code. These skills ensure efficient development of high-performance applications and successful integration of GPU acceleration into software solutions.

What are common challenges faced by assistant CUDA developers when optimizing code for GPU performance?

Assistant CUDA developers often encounter challenges such as managing memory efficiently between the host and device, ensuring proper kernel parallelization, and avoiding thread divergence. Balancing occupancy and resource usage can also be tricky, as it requires a deep understanding of how CUDA schedules and executes threads. Collaborating closely with data scientists and other engineers is essential to identify performance bottlenecks and implement effective optimizations.

What is the difference between Assistant Cuda vs Assistant Data Analyst?

AspectAssistant CudaAssistant Data Analyst
Required CredentialsTypically a relevant degree in computer science or related fieldOften a degree in data science, statistics, or related field
Work EnvironmentTech companies, software development teams, AI projectsBusiness, finance, marketing, or research departments
Employer & Industry UsageUsed in tech and AI industries for supporting CUDA programming tasksCommon in data-driven industries for data processing and analysis

Assistant Cuda and Assistant Data Analyst roles share some technical background but differ mainly in focus. Assistant Cuda primarily supports GPU programming and AI development, while Assistant Data Analyst focuses on data interpretation and reporting. Both roles require relevant technical skills and are found in industries leveraging data and technology, but their daily tasks and industry applications vary significantly.

What are the most commonly searched types of Cuda jobs in California?

The most popular types of Cuda jobs in California are:

What are popular job titles related to Assistant Cuda jobs in California?

For Assistant Cuda jobs in California, the most frequently searched job titles are:

What job categories do people searching Assistant Cuda jobs in California look for?

The top searched job categories for Assistant Cuda jobs in California are:

What cities in California are hiring for Assistant Cuda jobs?

Cities in California with the most Assistant Cuda job openings:

Assistant Cuda jobs near you

Onboard AV Software Engineer

San Francisco, CA • On-site, Remote

Humble Robotics
See more jobs at Humble Roboticsexternal

Full-time

Re-posted 16 days ago

Job description

About Humble Robotics
Working at Humble Robotics means taking on the biggest change in ground transportation in decades. We're building an autonomous, zero-emissions hauler that dramatically lowers the cost of freight with groundbreaking vision-based AI, designed for today's global logistics network.
We're a fast-moving, close-knit team of AV industry veterans and innovative thinkers. We don't believe culture can be engineered - but when it falls into place, it's a once-in-a-lifetime adventure.
Progress has never felt so present.
Position Overview
We're looking for a software engineer to optimize and deploy ML models on our trucks' onboard compute, and to own performance across the full autonomous driving stack. You'll take models from our ML team and make them run fast, efficiently, and reliably on embedded GPUs-using TensorRT, custom CUDA kernels, and low-level systems engineering. Beyond inference, you'll profile and optimize the entire onboard software pipeline to meet hard real-time deadlines. This is a rare chance to bridge ML and embedded systems for production autonomous freight, with the freedom and responsibility that comes with a small team tackling a massive problem.
Key Responsibilities
  • Optimize and deploy neural network models for onboard inference using TensorRT and custom CUDA kernels
  • Profile and reduce end-to-end latency across the autonomous driving stack-from sensor ingestion to control
  • Build and maintain the onboard C++ and Rust software infrastructure, including real-time data pipelines, inter-process communication, and hardware abstraction layers
  • Implement model quantization, pruning, and other optimization techniques to maximize throughput on embedded GPU platforms
  • Collaborate with ML engineers to ensure models are designed for efficient deployment, and with vehicle systems engineers to meet real-time safety constraints

Minimum Qualifications
  • BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field-or equivalent industry experience
  • Strong proficiency in C++ and/or Rust for performance-critical systems
  • Hands-on experience with GPU-accelerated computing-CUDA, TensorRT, or similar inference optimization toolchains
  • Familiarity with ML model architectures (transformers, CNNs) and the ability to reason about computational cost and memory footprint
  • Eligible to work in the United States

  • Experience with onboard software for autonomous vehicles, robotics, or IoT/edge devices
  • Deep knowledge of CUDA, TensorRT, model quantization, and kernel-level optimization
  • Experience with Bazel or similar build systems for complex codebases
  • Familiarity with real-time robotic systems
  • Experience profiling and optimizing full-system performance (CPU, GPU, memory, I/O) on embedded platforms
  • Comfort operating as an early team member-high ownership, low ego, fast iteration

Compensation
This role is eligible for base salary \+ benefits \+ equity compensation. Salary ranges are determined by role, level, and location. Within the range, individual pay is determined by additional factors, including qualifications, skills, experience, and location.
Additional Information
As part of the interview process, we may use Artificial Intelligence (AI) tools to compare your qualifications and experience to the job description. A human reviews all AI output and makes a final hiring decision. Humble Robotics does not rely on the output to make any employment decisions. Some applicants may have a legal right to opt-out of the use of AI as part of our interview process. Contact **[email protected]** to exercise this right or if you have further questions on the use of AI tools in our hiring process.
Humble Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law. Humble Robotics will consider qualified applicants with arrest and conviction records in a manner consistent with local ordinances.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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