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Assistant Cuda Jobs in California (NOW HIRING)

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Software Engineer - C++ GPU Performance

San Diego, CA · On-site

$168K - $239K/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 ...

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Assistant Cuda information

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 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 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 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.
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Assistant Cuda jobs near you

System Software Engineer -- GPU & Accelerated Compute

Sunday

Redwood City, CA • On-site

$211K - $251K/yr

Full-time

Re-posted 12 days ago

Job description

Job Summary:
Sunday is developing personal robots to reclaim the hours lost to repetitive tasks, aiming to make generalized robots broadly accessible. They are seeking a System Software Engineer focused on GPU and accelerated compute to own the accelerated compute layer of their ML & Robotics Infra, ensuring efficient model execution and GPU scheduling for real-time robotic systems.
Responsibilities:
• You’ll own and contribute to the accelerated compute layer of the ML & Robotics Infra, including:
• Efficient model execution and switching: Reduce gpu kernel launch overheads and make swapping between models on the same device fast and predictable
• GPU scheduling and time-slicing: Arbitrate GPU access across concurrent users (model inference, SLAM, and other robotics applications) with predictable latency
• Camera pipeline: Drive low-latency transfer of camera frames into GPU memory, integrating with HW accelerate encode/decode (NVDEC/NVENC) where appropriate
• CPU ↔ GPU data transfer: Build efficient, low-overhead data movement between host and device, including pinned memory, zero-copy paths, and asynchronous transfer patterns
• CPU/GPU synchronization: Design synchronization primitives and patterns that minimize stalls and keep inference pipelines full
Qualifications:
Required:
• 2+ years of experience developing gpu systems software
• Strong proficiency in CUDA and a systems language such as C++, C, or Rust
• Solid understanding of GPU architecture, GPU workloads, and the tradeoffs involved in time-slicing and sharing the device across users
• Hands-on experience with the CUDA ecosystem: CUDA runtime API, CUDA Graphs, and CUDA IPC
• Familiarity with GPU sharing mechanisms such as MPS and MIG
• Experience with GPU profiling tools such as Nsight Systems and Nsight Compute
• Solid Linux fundamentals: scheduling, IPC, memory management, and performance tuning
Preferred:
• Contributions to CUDA libraries or other GPU programming libraries
• Experience with camera pipeline integration and NVDEC/NVENC
• Experience optimizing model inference on embedded GPU platforms (e.g., Jetson)
• Experience with observability and tracing for GPU-accelerated workloads
Company:
Sunday is a robotics and artificial intelligence company that develops an autonomous home robot to assist with household tasks. Founded in 2024, the company is headquartered in Mountain View, USA, with a team of 51-200 employees. The company is currently Early Stage.

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