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Parallel Computing Jobs (NOW HIRING)

Nvidia

Senior GPU Architect

Durham, NC · On-site

$125K - $170K/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

$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 ...

Nvidia

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 ...

Nvidia

Senior GPU Architect

Westford, MA · On-site

$134K - $182K/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

Austin, TX

$128K - $174K/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, Deep Learning

Austin, TX · On-site

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 deep learning and parallel processing algorithms. We ...

Nvidia

Senior GPU Architect, Deep Learning

Durham, NC · On-site

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 deep learning and parallel processing algorithms. We ...

Staffingine LLC

PyTorch Role/C++,Python

Bellevue, WA · On-site

Parallel computing models * Memory and compute optimization * Experience with: * PyTorch (model-level workloads and execution) * Triton (custom kernel development / compiler-level interaction)

ConfigUSA

CUDA Programmer

Waukesha, WI · On-site

Strong experience in CUDA programming and parallel computing concepts. * In-depth understanding of NVIDIA GPU architecture (threads, warps, SMs, memory hierarchy). * Proficiency in C/C++ for high ...

Nvidia

Senior Deep Learning Performance Architect

Austin, TX · On-site

$165K/yr

Develop innovative HW architectures to extend the state of the art in parallel computing performance, energy efficiency and programmability. * Benchmark and analyze AI workloads in single and multi ...

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Parallel Computing information

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$25K

$52.4K

$90.5K

How much do parallel computing jobs pay per year?

As of Aug 22, 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 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 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 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 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.

Is parallel computing hard?

Parallel computing as a job involves designing and managing systems that perform multiple calculations simultaneously, which requires strong problem-solving skills, knowledge of algorithms, and proficiency with programming tools like MPI or OpenMP. The difficulty depends on the complexity of tasks and the level of expertise, but it generally involves understanding concurrency, synchronization, and performance optimization. Gaining experience through coursework, certifications, and hands-on projects can help reduce the learning curve.
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Parallel Computing jobs near you
Infographic showing various Parallel Computing job openings in the United States as of August 2026, with employment types broken down into 25% Full Time, and 75% Contract. Highlights an 100% In-person job distribution, with an average salary of $52,360 per year, or $25.2 per hour.

Autonomous Driving Vehicle Perception Engineer

Reveille Technologies

Northville, MI • On-site

Other

Posted 16 days ago

Job description

ONLY FULLTIME NO CONTRACT

Job Title: Autonomous Driving Vehicle Perception Engineer

Location: Northville, MI (Onsite)

Type: Full-time

About the Role

We are seeking an experienced Perception Engineer to design, build, and deploy real-time perception and multi-sensor fusion algorithms for next-generation autonomous driving systems across LiDAR, camera, radar, and GNSS modalities.

Key Responsibilities

  • Algorithms & Models: 3D Object Detection, Multi-Object Tracking, Semantic Segmentation, Machine Learning (PyTorch, TensorFlow, YOLO, Faster R-CNN, DeepSORT).

  • Localization & SLAM: Graph SLAM, LIO-SAM, Visual-Inertial SLAM, Point Cloud Processing (PCL, Open3D).

  • Sensor Fusion & Calibration: Intrinsic & Extrinsic Calibration, Multi-Sensor Fusion (LiDAR, Camera, Radar, GNSS), Coordinate Transformations, Time Synchronization.

  • System Integration & Optimization: ROS2, C++, Python, Real-Time Systems, Parallel Computing (CUDA, OpenCL).

Key Requirements

  • Experience: 3+ years in sensor calibration, multi-sensor fusion, or autonomous vehicle perception.

  • Core Fundamentals: Strong background in 3D geometry, coordinate frames, quaternions, probability, Bayesian filtering, and data association.

  • Tech Stack: High proficiency in C++ and Python; hands-on experience with ROS2 and computer vision libraries (OpenCV, PCL, or Open3D).

  • Deep Learning & Tracking: Experience with PyTorch/TensorFlow, object detection models (YOLO, Faster R-CNN), and tracking algorithms (Kalman Filters, DeepSORT, UKF).

  • Optimization: Familiarity with parallel computing platforms (CUDA/OpenCL) for real-time performance.

Thanks and Regards,

Praveenkumar

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