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Parallel Computing Jobs (NOW HIRING)

Nvidia

Senior GPU Architect

Santa Clara, CA

$152.10K - $206.70K/yr

A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for parallel processing algorithms. We are constantly ...

Nvidia

Senior GPU Architect

Durham, NC

$125.10K - $170.10K/yr

A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for parallel processing algorithms. We are constantly ...

Nvidia Corporation

Senior GPU Architect

Santa Clara, CA · On-site

$152.10K - $206.70K/yr

A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for parallel processing algorithms. We are constantly ...

Nvidia

Senior GPU Architect

Westford, MA

$134.60K - $182.90K/yr

A key part of NVIDIA's strength is to innovate in the graphics and parallel computing fields delivering the highest performance in the world for parallel processing algorithms. We are constantly ...

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$25K

$52.4K

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How much do parallel computing jobs pay per year?

As of May 30, 2026, the average yearly pay for parallel computing in the United States is $52,360.00, according to ZipRecruiter salary data. Most workers in this role earn between $40,000.00 and $59,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Parallel Computing Specialist, and why are they important?

To thrive as a Parallel Computing Specialist, you need strong knowledge of computer architecture, parallel algorithms, and experience with programming languages such as C/C++, Python, and frameworks like MPI or OpenMP, often supported by a degree in computer science or a related field. Familiarity with high-performance computing (HPC) environments, GPU programming (CUDA, OpenCL), and cloud-based parallel processing systems is typically required. Analytical thinking, problem-solving abilities, and effective collaboration are crucial soft skills in this role. These skills are vital for efficiently designing, optimizing, and implementing solutions that leverage parallelism to significantly accelerate computational tasks.

What are some common challenges faced by professionals working in parallel computing roles?

Professionals in parallel computing often encounter challenges such as efficiently dividing complex tasks among multiple processors and minimizing communication overhead between them. Debugging and optimizing performance across parallel architectures can be difficult, as issues like race conditions and load imbalances frequently arise. Additionally, staying current with evolving hardware technologies and parallel programming frameworks is essential to ensure solutions remain efficient and scalable. Collaborating with cross-functional teams, such as data scientists and system architects, is also crucial for integrating parallel solutions into larger projects.

What is parallel computing?

Parallel computing is a type of computation where many calculations or processes are carried out simultaneously, leveraging multiple processors or computers to solve complex problems more efficiently. It divides large tasks into smaller ones that can be executed concurrently, significantly speeding up processing time. Commonly used in scientific research, data analysis, and engineering, parallel computing is essential for handling large-scale simulations and big data applications.

What is the difference between Parallel Computing vs Data Analyst?

AspectParallel ComputingData Analyst
Required CredentialsComputer Science or Engineering degree, programming skillsStatistics, Data Science, or related degree, analytical skills
Work EnvironmentResearch labs, tech companies, high-performance computing centersBusiness, finance, healthcare, corporate offices
Industry UsageTechnology, research, scientific computingBusiness intelligence, market analysis, reporting

While Parallel Computing focuses on developing algorithms to process large data sets efficiently across multiple processors, Data Analysts interpret data to provide actionable insights. Both roles require strong technical skills but serve different purposes: one enhances computational performance, the other informs business decisions.

More about Parallel Computing jobs
What cities are hiring for Parallel Computing jobs? Cities with the most Parallel Computing job openings:
What states have the most Parallel Computing jobs? States with the most job openings for Parallel Computing jobs include:
What job categories do people searching Parallel Computing jobs look for? The top searched job categories for Parallel Computing jobs are:
Parallel Computing jobs near you
Infographic showing various Parallel Computing job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Hybrid job distribution, with an average salary of $52,360 per year, or $25.2 per hour.

GPU Systems Engineer - HPC / Parallel Computing

Vast.ai

San Francisco, CA

$160K - $320K/yr

Full-time

Medical, Dental, Vision, Life, Retirement

Posted 3 days ago

Job description

About Us

Vast.ai's cloud powers AI projects and businesses all over the world. We are democratizing and decentralizing AI computing—reshaping our future for the benefit of humanity.

We are a small, growing, and highly motivated team dedicated to an ambitious technical plan. We operate with a flat mobile organizational structure where all contribute directly to the company's mission. Leadership is earned by those who show initiative and deliver excellence.

We seek engineers/researchers with strong intrinsic drive, a true passion for advancing the state of the art, and a mix of excellent research, coding, and communication skills.

LOCATION: On-site at our office in San Francisco or Westwood, Los Angeles.

About the Role

We're looking for a systems engineer with HPC or parallel programming experience to help scale AI inference. You'll leverage your knowledge of high-performance systems to optimize GPU performance at the bleeding edge of AI.

  • Full-Time
  • On-site at either our SF or LA offices
Tech Stack

CUDA/C++, GPGPU, Python, Linux

Key Responsibilities
  • Design and optimize GPU kernels and tensor libraries
  • Translate HPC techniques into scalable AI inference solutions
  • Evaluate emerging architectures and resource management approaches
  • Collaborate with technical leadership to improve GPU infrastructure efficiency
Ideal Experience
  • Advanced C++ (C++17/20 preferred)
  • Expertise with at least one parallel framework (CUDA, HIP, SYCL, OpenCL, OpenACC, or similar)
  • Strong background in systems optimization and HPC performance tooling
  • Familiarity with distributed training/inference frameworks (bonus)
Interview Process

After submitting your application, our technical team reviews your credentials. If selected, you'll proceed through the following stages:

  • Initial screening (virtual, 15 minutes)
  • Quick dive into Vast, systems and architectures (virtual, 30 minutes)
  • LLM-assisted coding assessment (virtual, 1 hour)
  • Meet and greet with coding assessment (on-site, 2 hours)
Our goal is to complete the interview process in two weeks.Annual Salary Range

$160,000 – $320,000 + equity + benefits

Vast.ai is hiring across all experience levels with compensation commensurate with background, experience and potential.

Benefits
  • Comprehensive health, dental, vision, and life insurance
  • 401(k) with company match
  • Meaningful early-stage equity
  • Onsite meals, snacks, and close collaboration with founders/tech leaders
  • Ambitious, fast-paced startup culture where initiative is rewarded