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

Monitor compute system performance across CPU, GPU, memory, I/O, and networking. What You Have * Education: B.S., M.S., or Ph.D. in Computer Engineering, Robotics, Electrical Engineering, or a ...

Monitor compute system performance across CPU, GPU, memory, I/O, and networking. What You Have * Education: B.S., M.S., or Ph.D. in Computer Engineering, Robotics, Electrical Engineering, or a ...

Solutions Engineer, Routing- I&MI

Irvine, CA · On-site

$205K - $266K/yr

Scalable fabrics for GPU-powered infrastructures Key Responsibilities Technical Leadership & Pre ... Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of ...

Director of Solutions Engineering

Irvine, CA · On-site

$175K - $215K/yr

Experience supporting highly regulated or performance-sensitive environments (e.g., financial ... Compute (rack, blade, HCI, GPU-enabled workloads) * Enterprise storage (SAN/NAS/NVMe) * Modern ...

Helpdesk Technician

Anaheim, CA · On-site

$70K - $80K/yr

Signia Aerospace is a global, integrated provider of high-performance systems and specialized ... Experience in manufacturing or engineering environments, including applications such as CAD, CAM ...

Program Manager

Anaheim, CA · On-site

$150K - $170K/yr

Signia Aerospace is a global, integrated provider of high-performance systems and specialized ... Translate customer requirements into executable plans across engineering, manufacturing, supply ...

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Gpu Performance Engineer information

See Riverside, CA salary details

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How much do gpu performance engineer jobs pay per hour?

As of Jun 26, 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 GPU Performance Engineers 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 cities near Riverside, CA are hiring for Gpu Performance Engineer jobs? Cities near Riverside, CA with the most Gpu Performance Engineer job openings:

Senior Machine Learning Platform Engineer

FieldAI

Irvine, CA

$112K - $154K/yr

Full-time

Posted 12 days ago


Job description

FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
What You’ll Get To Do:
  • Design and manage scalable ML infrastructure with IaC tools (Terraform, CloudFormation).
  • Develop and optimize cloud-based pipelines for training, evaluation, and inference on multimodal datasets.
  • Build and operate data systems for large-scale video ingestion, indexing, and storage.
  • Maintain MLOps workflows for versioning, experiment tracking, reproducibility, and CI/CD.
  • Ensure reliability and observability with monitoring, logging, and alerting.
  • Collaborate with AI/ML Engineers to productionize workflows.
  • Optimize infrastructure for performance and cost across cloud and edge.
  • Enforce best practices in security, compliance, and maintainability.
  • Mentor and manage junior engineers, providing technical guidance and career development.
What You Have:
  • Bachelor’s/Master’s in Computer Science, Engineering, or related field (or equivalent experience).
  • 4+ years of industry experience in ML infrastructure or platform engineering.
  • Strong coding skills in Python/TypeScript and a strong foundation in software engineering best practices.
  • Proven experience with distributed systems, cloud platforms (AWS preferred), containerization and orchestration (Docker, Kubernetes/EKS, Ray), and serverless.
  • Hands-on experience building ML pipelines for distributed training and large-scale inference.
  • Strong knowledge of data management at scale, including preprocessing and retrieval of video/image datasets.
  • Proficiency with CI/CD pipelines, infrastructure-as-code (Terraform, CloudFormation), and automation.
  • Familiarity with MLOps tools (MLflow, Kubeflow, Airflow).
  • Experience with system monitoring and observability in production.
The Extras That Set You Apart:
  • Experience with vector databases (OpenSearch, Pinecone, Weaviate) for indexing and retrieval.
  • Familiarity with distributed training frameworks (Horovod, DDP/FSDP, DeepSpeed, Ray).
  • Hands-on experience with GPU orchestration and auto-scaling (Karpenter, SageMaker, EKS).
  • Experience with agentic AI deployment workflows, orchestration frameworks, and retrieval-augmented generation.
  • Strong knowledge of security and compliance in ML and cloud environments.
Our salary range is generous and we consider each individual’s background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.

Why Join FieldAI in Irvine?
In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics’ hardest challenges: reliable deployment outside the lab. Our Field Foundational Models™ raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use.
You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments.

Be Part of the Next Robotics Revolution
We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world.

Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like.

We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected statu

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.