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Gpu Performance Engineer Jobs in Riverside, CA (NOW HIRING)

FieldAI

Software Engineer, MLOps

Irvine, CA · On-site

Responsibilities : • Design, build, and maintain GPU based infrastructure for machine learning ... model performance and system reliability across development and production environments. • ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site

$129K - $171K/yr

Responsibilities : • Design, build, and maintain GPU based infrastructure for machine learning ... model performance and system reliability across development and production environments. • ...

Socket.dev

Principal Solutions Architect (Req#1341)

Irvine, CA · On-site

$170 - $190/hr

  • Medical

  • Retirement

  • PTO

... engineered infrastructure solutions spanning colocation facilities, GPU compute, high-performance networking, parallel storage, and the complete NVIDIA AI software stack. You will serve as a trusted ...

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site

$131K - $173K/yr

Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data ... Monitor model performance and system reliability across development and production environments.

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site +1

$131K - $173K/yr

Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data ... Monitor model performance and system reliability across development and production environments.

FieldAI

Senior Software Engineer, MLOps

Irvine, CA · On-site +1

$131K - $173K/yr

Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data ... Monitor model performance and system reliability across development and production environments.

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Gpu Performance Engineer information

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How much do gpu performance engineer jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for gpu performance engineer in Riverside, CA is $62.71, according to ZipRecruiter salary data. Most workers in this role earn between $51.39 and $70.96 per hour, depending on experience, location, and employer.

What are some common challenges faced by a GPU performance engineer when optimizing graphics workloads?

GPU Performance Engineers often encounter challenges such as identifying performance bottlenecks within complex graphics pipelines, balancing resource utilization, and achieving optimal frame rates across diverse hardware configurations. They must use specialized profiling tools and collaborate closely with developers, driver engineers, and QA teams to address issues like memory bandwidth limitations or shader inefficiencies. Staying updated with rapidly evolving GPU architectures and optimizing for both current and next-generation hardware are also key aspects of the role.

What is a GPU performance engineer?

A GPU Performance Engineer is a specialist who analyzes, optimizes, and improves the performance of graphics processing units (GPUs). They work on identifying bottlenecks, optimizing code, and ensuring that GPU hardware and software deliver maximum efficiency and speed. Their role may involve working with drivers, firmware, and applications to enhance graphics and compute workloads. This job is essential in industries like gaming, AI, and high-performance computing where GPU efficiency directly impacts user experience and system performance.

What are the key skills and qualifications needed to thrive as a GPU performance engineer, and why are they important?

To thrive as a GPU Performance Engineer, you need a strong background in computer architecture, programming (C/C++), and a degree in computer science, electrical engineering, or a related field. Proficiency with GPU profiling tools (e.g., NVIDIA Nsight, AMD Radeon GPU Profiler), performance analysis frameworks, and parallel computing libraries like CUDA or OpenCL is typically required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with developers and debugging performance bottlenecks. These skills and qualities are essential for optimizing GPU performance, ensuring efficient software-hardware interaction, and delivering high-quality graphics or compute solutions.

What is the difference between Gpu Performance Engineer vs Gpu Hardware Engineer?

AspectGpu Performance EngineerGpu Hardware Engineer
Primary FocusOptimizing GPU performance, benchmarking, and tuning softwareDesigning, developing, and testing GPU hardware components
Required SkillsProgramming, performance analysis, GPU architecture knowledgeHardware design, circuit analysis, FPGA/ASIC experience
Work EnvironmentSoftware development teams, labs for testing performanceHardware labs, manufacturing facilities, R&D centers
Common CertificationsNone specific, often requires computer engineering or related degreesElectrical engineering, VLSI design certifications

The Gpu Performance Engineer primarily focuses on optimizing and testing GPU software performance, while the Gpu Hardware Engineer designs and develops the physical GPU components. Both roles require a strong background in computer engineering, but differ in their core responsibilities and work environments.

What are popular job titles related to Gpu Performance Engineer jobs in Riverside, CA?

For Gpu Performance Engineer jobs in Riverside, CA, the most frequently searched job titles are:

What job categories do people searching Gpu Performance Engineer jobs in Riverside, CA look for?

The top searched job categories for Gpu Performance Engineer jobs in Riverside, CA are:

What cities near Riverside, CA are hiring for Gpu Performance Engineer jobs?

Cities near Riverside, CA with the most Gpu Performance Engineer job openings:

Gpu Performance Engineer jobs near you

Software Engineer, MLOps

FieldAI

Irvine, CA • On-site

Full-time

Re-posted 19 days ago

Job description

Job Summary:
FieldAI is transforming how robots interact with the real world by building reliable AI systems for complex robotics challenges. The MLOps Engineer will design and maintain infrastructure for machine learning systems, collaborating closely with engineering teams to ensure effective deployment and monitoring of ML models.
Responsibilities:
• Design, build, and maintain GPU based infrastructure for machine learning pipelines, including data processing, training, evaluation, inference and deployment workflows.
• Collaborate closely with robotics teams to implement model serving infrastructure for edge/robot deployment.
• Build tools and automation to support reproducible experiments, model versioning, and dataset management.
• Deploy and manage ML services and inference pipelines using containerized environments for efficient scaling and scheduling of heterogeneous compute resources.
• Monitor model performance and system reliability across development and production environments.
• Improve the efficiency, scalability, and reliability of ML workflows and infrastructure.
• Work with cross-functional engineering teams to integrate ML components into robotics software systems.
Qualifications:
Required:
• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent work experience).
• 3-7 years of experience in MLOps, machine learning infrastructure, or related engineering roles.
• Strong programming skills in Python or similar languages.
• Experience building and maintaining machine learning pipelines.
• Hands-on experience with cloud and cloud-native tools such as AWS (SageMaker, S3, or similar cloud ML services), Kubernetes etc.
• Solid understanding of Linux systems and distributed computing environments.
• Experience with GPU workload scheduling and orchestration across multi-region cloud environments.
• Excellent problem-solving skills and the ability to work collaboratively in a team environment.
Preferred:
• Experience deploying and operating ML systems for robotics or real-world physical systems.
• Experience with scaling AI, ML, and inference workloads on Kubernetes.
• Exposure to ROS-based robotics data formats and pipelines (rosbags, point clouds)
• Experience with experiment tracking, model versioning, or dataset versioning tools.
• Experience optimizing ML pipelines for large-scale training and data processing.
• Experience working closely with research or applied machine learning teams.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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