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Parallel Jobs in Santa Clara, CA (NOW HIRING)

GA Recruiter

Palo Alto, CA · On-site

$180 - $240/hr

About us Parallel is a web infrastructure company. Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful ...

GTM Leader, Enterprise

Palo Alto, CA · On-site

$180 - $300/hr

About us Parallel is a web infrastructure company. Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful ...

Senior GPU Architect

Santa Clara, CA · On-site

$184 - $356.50/hr

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 ...

Senior GPU Architect

Santa Clara, CA · On-site

$152K - $206K/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 ...

Senior GPU Architect

Santa Clara, CA · On-site

$152K - $206K/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 ...

Senior GPU Architect

Santa Clara, CA

$152K - $206K/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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Parallel information

See Santa Clara, CA salary details

$29.4K

$61.5K

$106.3K

How much do parallel jobs pay per year?

As of Sep 5, 2026, the average yearly pay for parallel in Santa Clara, CA is $61,493.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,000.00 and $69,900.00 per year, depending on experience, location, and employer.

What is a parallel?

In the context of computing and technology, 'Parallel jobs' refer to tasks or processes that are executed simultaneously across multiple processors or computers. This approach is commonly used in high-performance computing (HPC), data processing, and scientific research to speed up complex computations by breaking them into smaller, concurrent tasks. Parallel jobs can significantly reduce the time required to process large datasets or perform intensive calculations. They are managed using parallel computing frameworks and often require specialized software and hardware to coordinate the execution of multiple processes. Understanding how to design and manage parallel jobs is essential for roles in data science, engineering, and research fields.

What skills and qualifications are needed to thrive as a parallel?

To thrive as a Parallel Computing Engineer, you need a strong background in computer science, mathematics, and parallel algorithms, often supported by a relevant degree. Proficiency with parallel programming languages (such as CUDA, OpenMP, or MPI), high-performance computing (HPC) clusters, and debugging tools is essential. Strong analytical thinking, collaborative teamwork, and effective problem-solving skills help you stand out in this field. These skills are vital for optimizing computational processes and ensuring efficient, scalable solutions in complex computing environments.

What are common challenges faced by professionals working in parallel computing roles, and how can they be addressed?

Professionals in parallel computing roles often encounter challenges such as debugging complex, concurrent code and optimizing performance across multiple processors. These issues require a solid understanding of parallel algorithms and experience with tools designed for performance profiling and debugging. Collaboration with team members is essential, as projects typically involve working closely with software engineers, system architects, and hardware specialists. To address these challenges, it's helpful to stay current with best practices, participate in code reviews, and leverage community resources and documentation.

What is the difference between Parallel vs Network Engineer?

AspectParallelNetwork Engineer
Required CertificationsCompTIA A+, Cisco CCNA, Network+CCNA, CCNP, CompTIA Network+
Work EnvironmentData centers, server rooms, cloud environmentsCorporate offices, data centers, ISPs
Industry UsageIT, cloud services, data managementTelecommunications, IT, enterprise networks
Common Search/ComparisonParallel vs Network Engineer

Parallel and Network Engineer roles share similar certifications and work environments, often overlapping in IT and data management sectors. However, Parallel roles focus more on parallel processing and computing tasks, while Network Engineers specialize in designing and maintaining network infrastructure. Understanding these differences helps job seekers identify the right career path based on their skills and interests.

What cities near Santa Clara, CA are hiring for Parallel jobs?

Cities near Santa Clara, CA with the most Parallel job openings:

Infographic showing various Parallel job openings in Santa Clara, CA as of August 2026, with employment types broken down into 2% As Needed, 78% Full Time, 17% Part Time, 1% Temporary, and 2% Contract. Highlights an 72% Physical, 6% Hybrid, and 22% Remote job distribution, with an average salary of $61,493 per year, or $29.6 per hour.

Software Engineer, Parallel Scientific Computing

Vorticity Inc.

Redwood City, CA • On-site

$140 - $200/hr

Other

Posted 25 days ago


Job description

We're building a new class of Scientific Processing Units (SPUs) to push the boundaries of High-Performance Computing (HPC). In this role, you'll develop and optimize the core computational kernels needed to run scientific applications across a wide range of domains on our custom parallel SPU architecture, from simulation and emulation to real hardware.

You'll work across abstraction layers. Starting from the mathematics and algorithms behind an application, building high-level reference implementations, translating them into parallel kernels, and optimizing them against our architecture. You should be comfortable moving between a mathematical description of a problem, C++/Python reference code, low-level parallel software, and the hardware that executes it.

This is not an easy role. It requires passion for understanding how hardware and software work together, the ability to wear multiple hats, strong communication skills, a lot of patience, independence, and, most importantly, zero ego. Why? Because this is the first time something like this has ever been attempted.

Responsibilities
  • Develop high-level reference implementations of scientific applications (e.g. finite-difference time-domain methods, computational fluid dynamics, electromagnetic wave propagation, etc.) from mathematical and algorithmic descriptions.
  • Implement and parallelize scientific applications using our proprietary Software Development Kit (SDK) across SPU simulation, emulation, and real-hardware environments.
  • Develop and optimize performance critical kernels for the SPU architecture.
  • Own iterative performance optimization loops, benchmark, profile, identify bottlenecks, implement improvements and repeat, until we reach our performance targets.
  • Understand applications across abstraction layers, from their mathematical formulation to their mapping onto parallel hardware.
  • Identify opportunities to improve our compilation flow, runtime, hardware utilization, and overall system efficiency.
  • Collaborate with architecture and performance teams to analyze bottlenecks and co-design innovative software and hardware solutions.
  • Independently identify problems and opportunities, propose next steps, and drive work forward without requiring detailed task by task direction.
  • Write clear, concise technical documentation for Vorticity’s engineers and customers.
  • Stay current with parallel programming models, High Performance Computing (HPC) architectures, numerical methods, and techniques for mapping scientific workloads onto parallel hardware.
Skills & Qualifications
  • Bachelor's degree in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or related field.
  • Master's or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or related field.
  • 5+ years of experience in modern C++ (CUDA C++ experience strongly preferred) for parallel programming and high-performance computing. Exceptional candidates with fewer years but strong skills are also welcome.
  • Ability to understand numerical algorithms and translate mathematical descriptions into working implementations. You don't need to be a mathematician, but a gradient, divergence, stencil, or multidimensional discretization should not scare you.
  • Ability and willingness to debug across abstraction layers, from an application or numerical algorithm down through software and into the underlying architecture.
  • Strong understanding of computer architecture and the interaction between software and hardware.
  • Proficiency with C++, Python, and Linux development environments.
  • Excellent written and verbal communication skills.
  • Strong ability to work independently and in a team, while taking ownership of ambiguous problems, and determining what needs to be done next.
  • Willingness to put in the hard work needed to bring our SPU to life.
  • Above all: zero ego.

As passionate scientists and engineers, we are well aware of the plethora of critical problems in the world that cannot be solved because humanity simply does not have enough computing power. To address this, Vorticity is developing a radically new silicon chip architecture and system to dramatically accelerate scientific computing problems.

Vorticity’s mission is to expand human ingenuity. To do that we are building a team of exceptional people to work together on big problems. Join us!

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