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Remote Gpu Programming Jobs in California (NOW HIRING)

Biohub

Staff HPC Engineer

San Francisco, CA · On-site +1

... remote dev, containerization, MLOps workflows). What You'll Bring Essential * Bachelor's or ... GPU, compute and storage systems). * Strong scripting/programming skills (Python, Bash) and ...

Nvidia

Senior Software Engineer - Topography

Santa Clara, CA · Remote

$143K - $189K/yr

Experience with GPU clusters, NVLink, InfiniBand, Ethernet fabrics, or HPC. * Hands-on work with ... If you're a creative, curious, and driven technical leader, we want to hear from you! #LI-Remote ...

Lambda

$185K - $290K/yr

Multiple Sites - US (Remote/Hybrid eligible) Travel : 50% travel to our data center sites Role ... GPU deployments at rack densities of 136-380 kW per rack. What You'll Do BMS / Controls ...

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Remote Gpu Programming information

What are some common challenges faced by professionals in remote GPU programming roles, and how can they be addressed?

Remote GPU programming roles often involve unique challenges such as managing high-latency connections to remote servers, troubleshooting hardware-specific issues without physical access, and ensuring code compatibility across different GPU architectures. Effective communication with distributed teams is crucial, as is using robust remote debugging tools and version control systems. Staying proactive with documentation and regularly syncing with team members can help address these obstacles and support successful project delivery.

What is remote GPU programming?

Remote GPU programming refers to the practice of developing and running code that utilizes graphics processing units (GPUs) on computers or servers that are accessed over a network, rather than on your local machine. This approach allows developers to leverage powerful, often cloud-based, GPU resources to handle computationally intensive tasks like machine learning, scientific simulations, or rendering without needing specialized hardware themselves. It often involves using remote desktop tools, cloud platforms, or custom APIs to access and manage GPU resources remotely.

What are the key skills and qualifications needed to thrive as a remote GPU programmer?

To thrive as a Remote GPU Programmer, you need in-depth knowledge of parallel computing, proficiency in programming languages like C/C++, and experience with GPU architectures, often backed by a degree in computer science or a related field. Familiarity with technical tools such as CUDA, OpenCL, and GPU profiling/debugging systems is commonly required, along with certifications in GPU programming or high-performance computing. Strong problem-solving abilities, self-motivation, and effective remote communication skills help individuals excel in distributed teams. These competencies are crucial for efficiently developing and optimizing GPU-accelerated applications while collaborating across remote environments.
What are the most commonly searched types of Gpu Programming jobs in California? The most popular types of Gpu Programming jobs in California are:
What are popular job titles related to Remote Gpu Programming jobs in California? For Remote Gpu Programming jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Gpu Programming jobs in California look for? The top searched job categories for Remote Gpu Programming jobs in California are:
What cities in California are hiring for Remote Gpu Programming jobs? Cities in California with the most Remote Gpu Programming job openings:
Remote Gpu Programming jobs near you
Infographic showing various Remote Gpu Programming job openings in California as of August 2026, with employment types broken down into 70% Full Time, and 30% Contract. Highlights an 100% Remote job distribution.

Principal Infrastructure Architect

DigitalOcean

San Francisco, CA • Remote

$184K - $231K/yr

Full-time

Re-posted 22 days ago

Job description

Drive the future of DigitalOcean - design our Compute, GPU, Storage, and Infrastructure Platforms

This role is a Principal Infrastructure Architect on our Hardware Engineering team, this individual owns the forward looking roadmap for our CPU, GPU,Storage, and Infrastructure hardware platforms. Looking forward as the industry evolves, , from Air to Liquid to 800VDC rack scale systems. Collaborating with peers on the Infrastructure and Networking teams who own the roadmaps for datacenters and networking at DO.

What You'll Be Doing:
  • Defining and owning DigitalOcean's hardware architectures for next-generation compute environments across our CPU, GPU, Storage, and Infrastructure Server SKU products, navigating shifts in technology, networking topologies, and AI/ML requirements.
  • Serving as DigitalOcean's primary technical stakeholder for infrastructure technologies partnering with SMEs in the Product and Hardware Engineering organization.
  • Providing leadership and guidance as a primary stakeholder in the hardware selection and qualification process.
  • Evangelizing emerging technical concepts to senior leadership to inform product and business strategies.
  • Architecting end-to-end server designs with vendors and partner teams to support a wide variety of CPU, GPU, and other emerging workloads.
  • Architecting end-to-end storage topologies with vendor teams to support diverse workloads across performance, capacity, and archival tiers, moving from direct-attached to disaggregated and software-defined storage.
  • Leading proof-of-concept (PoC) initiatives for emerging infrastructure technologies and transitioning successful pilots into global deployment standards.
  • Mentor engineers at all levels and contribute to a culture of technical excellence, inclusivity, and impact.
  • Represent DigitalOcean in the broader community attending conferences, contributing to papers and presentations.
What We'll Expect From You:
  • 10+ years of experience in server architecture, storage architecture, infrastructure engineering, or a related field, with a track record of owning and deploying designs at scale.
  • Deep expertise in server and storage hardware platforms with strong working knowledge of compute technologies across the spectrum-from commodity CPU servers to rack scale GPU and xPU platforms.
  • Working experience of driving AI/ML hardware roadmaps and the ability to translate evolving silicon, accelerator, memory, and storage trends into future-proofed platform designs.
  • Knowledge of emerging and non-traditional hardware approaches such as CXL memory pooling, DPUs/SmartNICs, disaggregated storage, and computational storage.
  • Demonstrated ability to lead proof-of-concept initiatives and scale successful pilots into global deployment standards.
  • Strong cross-functional collaboration skills, partnering with technical and business teams along with external vendors and partners.
  • Excellent communication and leadership skills, including mentoring engineers and representing the company externally at conferences and through technical papers.
Compensation Range: 
  • $184,000 - $231,000

*This is a remote role

JR: 2026-7831

#LI-Remote

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