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Senior Gpu Engineer Jobs (NOW HIRING)

Nvidia

Senior GPU Raytracing Engineer

Santa Clara, CA

$122K - $168K/yr

We are seeking a selfmotivated senior engineer for the Aerial Omniverse Digital Twin team. This ... As a member of NVIDIA's Aerial team, you will architect and implement a GPU raytracing engine that ...

Nvidia

Senior GPU Memory Architect

Austin, TX

We are now looking for a Senior GPU Memory Architect. NVIDIA is seeking a motivated architect to ... Master degree or equivalent experience in Electrical Engineering, Computer Science, Computer ...

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Snarkify is seeking an experienced and highly skilled Senior GPU Performance Engineer to join our team and play a pivotal role in advancing the state-of-the-art in Zero-Knowledge Proof (ZKP ...

Qualcomm

Senior GPU Software Engineer

San Diego, CA · On-site

$130K - $171K/yr

... Engineering, or related field. • 2+ years of relevant GPU experience. • 1+ year of work experience in a role requiring interaction with senior leadership (e.g., Director and above). Principal ...

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Snarkify is seeking an experienced and highly skilled Senior GPU Performance Engineer to join our team and play a pivotal role in advancing the state-of-the-art in Zero-Knowledge Proof (ZKP ...

Nvidia

Senior GPU Memory Architect

Santa Clara, CA

We are now looking for a Senior GPU Memory Architect. NVIDIA is seeking a motivated architect to ... Master degree or equivalent experience in Electrical Engineering, Computer Science, Computer ...

Nvidia

Senior GPU Memory Architect

Durham, NC

We are now looking for a Senior GPU Memory Architect. NVIDIA is seeking a motivated architect to ... Master degree or equivalent experience in Electrical Engineering, Computer Science, Computer ...

Nvidia

Senior GPU Architect

Santa Clara, CA

$152K - $206K/yr

Join our technically diverse team of GPU architects, software engineers and deep learning experts to push the boundaries of AI performance! What you'll be doing: * Architect and plan features in ...

Nvidia Corporation

Senior GPU Architect

Santa Clara, CA · On-site

$152K - $206K/yr

Join our technically diverse team of GPU architects, software engineers and deep learning experts to push the boundaries of AI performance! What you'll be doing: * Architect and plan features in ...

Nvidia

Senior GPU Compiler Development Engineer

Austin, TX

$121K - $160K/yr

Work with NVIDIA GPU Architecture and CUDA Programming model teams to build abstractions to expose ... As a senior member of the team, you will be responsible for leading efforts to enhance PTX Compiler ...

Crusoe

Senior GPU Capacity Planner

San Francisco, CA · On-site

As the Senior GPU Capacity and Optimization Planner, you will own the day-to-day management ... You will work closely with Sales, Customer Success, Solutions Engineering, and Fleet Management to ...

Crusoe

Senior GPU Capacity Planner

Seattle, WA · On-site

As the Senior GPU Capacity and Optimization Planner, you will own the day-to-day management ... You will work closely with Sales, Customer Success, Solutions Engineering, and Fleet Management to ...

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Senior Gpu Engineer information

See salary details

$59.5K

$126.6K

$183.5K

How much do senior gpu engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for senior gpu engineer in the United States is $126,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,500.00 and $143,500.00 per year, depending on experience, location, and employer.

What is a senior GPU engineer?

Senior GPU Engineers are experienced professionals who design, develop, and optimize graphics processing units (GPUs) and related software. They work on hardware architecture, driver development, and performance optimization to ensure GPUs run efficiently for tasks like gaming, AI, and scientific computing. In addition to technical expertise, Senior GPU Engineers often mentor junior team members and collaborate with cross-functional teams to deliver high-performance solutions.

What are the key skills and qualifications needed to thrive as a senior GPU engineer?

To thrive as a Senior GPU Engineer, you need expertise in GPU architectures, parallel programming (such as CUDA or OpenCL), and a strong background in computer science or electrical engineering. Familiarity with performance profiling tools, hardware simulation environments, and experience with programming languages like C++ are typically required. Excellent problem-solving, teamwork, and communication skills help you collaborate effectively and tackle complex technical challenges. These skills are vital for developing high-performance GPU solutions and driving innovation in graphics and compute-intensive applications.

What are some common challenges senior GPU engineers face when optimizing performance for new hardware platforms?

Senior GPU Engineers often encounter challenges such as adapting software to leverage the latest GPU architectures, balancing performance with power efficiency, and debugging low-level hardware-related issues. Working closely with hardware designers and software developers is essential to ensure that drivers and applications are optimized for target platforms. Additionally, staying updated with rapidly evolving GPU technologies and industry standards is crucial for success in this role.

What is the difference between Senior Gpu Engineer vs Gpu Software Developer?

AspectSenior Gpu EngineerGpu Software Developer
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related field; experience with GPU architectureBachelor's or higher in Computer Science or related; programming skills in CUDA, OpenCL
Work EnvironmentResearch and development teams, hardware and software integrationSoftware development teams, application and driver development
Industry UsageHardware companies, gaming, AI, high-performance computingSoftware companies, game development, visualization

The main difference is that Senior Gpu Engineers focus on GPU hardware design, architecture, and optimization, while Gpu Software Developers primarily work on developing software applications, drivers, and APIs that utilize GPU capabilities. Both roles require strong programming skills and industry knowledge, but their focus areas differ significantly.

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Senior Gpu Engineer jobs near you
Infographic showing various Senior Gpu Engineer job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $126,557 per year, or $60.8 per hour.

Senior GPU Inference Performance Engineer

Advanced Micro Devices, Inc

Santa Clara, CA • On-site

$143K/yr

Full-time

Re-posted 10 days ago

Advanced Micro Devices rating

8.6

Company rating: 8.6 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

27th of 159 rated electronics manufacturers

Job description

WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges-striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.
THE ROLE:
We are looking for a Senior GPU Inference Performance Engineer to own end-to-end performance analysis of GPU-accelerated AI inference workloads. You will profile, diagnose, and explain performance across the full stack from GPU silicon, communication libraries, networking fabrics, and operating systems through the software runtime and drive competitive positioning against other accelerator vendors. This role sits at the intersection of hardware, systems software, networking, and AI infrastructure, and requires someone who can go deep on a trace and present findings to product and executive stakeholders.
THE PERSON:
A hands-on performance engineer who is equally comfortable reading a GPU trace, debugging distributed systems performance issues, and briefing executives. You are curious, evidence-driven, rigorous, and you don't stop at "X is faster" and you explain why, rooted in hardware and software evidence. You collaborate across hardware, systems software, networking, and AI infrastructure teams, communicate clearly in written reports and presentations, and thrive at the intersection of silicon, operating systems, communication libraries, networking, and AI. Experience with Linux systems, distributed GPU infrastructure, RDMA/RoCE networking, or communication libraries such as NCCL/RCCL is highly valued.
KEY RESPONSIBILITIES:
  • Full-stack GPU profiling: Instrument and analyze inference workloads across AMD Instinct (ROCm, rocProfiler, ROCm Systems Profiler, RGP, rocprof-compute, rocprof-sys, Omniperf) and NVIDIA (CUDA, Nsight Systems/Compute, DCGM) GPUs. Identify bottlenecks spanning HBM bandwidth, compute utilization, kernel scheduling, memory allocation, PCIe/Infinity Fabric data movement, and GPU runtime behavior.
  • Systems and runtime performance analysis: Profile and diagnose performance interactions between GPU runtimes, Linux operating systems, device drivers, container runtimes, memory subsystems, CPU scheduling, NUMA topology, and I/O pathways. Identify system-level bottlenecks that impact throughput, latency, and GPU utilization.
  • Competitive performance analysis: Design and execute head-to-head benchmarks (AMD vs. NVIDIA) on standardized AI and LLM workloads. Produce clear, data-backed explanations of why performance differs attributing gaps to hardware architecture, networking topology, communication libraries, software maturity, runtime behavior, or configuration differences.
  • Multi-server inference networking: Profile and optimize distributed inference topologies including prefill-decode (PD) disaggregation, pipeline parallelism, and tensor parallelism across multi-node clusters. Analyze network-level bottlenecks using RDMA/RoCE traces, NCCL/RCCL collective profiling, GPUDirect RDMA, NIC-level counters (Pensando, ConnectX), and network performance tools. Quantify the impact of latency, bandwidth, congestion, and topology on end-to-end inference SLAs.
  • GPU operator and Kubernetes stack: Profile the overhead introduced by GPU operators, device plugins, container runtimes (Docker, containerd), and Kubernetes scheduling on inference latency. Identify and resolve jitter, cold-start, resource contention, and infrastructure inefficiencies in production environments.
  • Tooling and automation: Build reproducible benchmarking harnesses, profiling scripts, and performance regression dashboards. Automate trace collection and analysis to support continuous performance validation across firmware, drivers, networking stacks, runtimes, and AI frameworks.

PREFERRED EXPERIENCE:
  • Background in GPU performance engineering, HPC, distributed systems, networking, operating systems, or systems performance analysis.
  • Hands-on proficiency with either AMD (ROCm, rocProfiler, ROCm Systems Profiler, RGP, rocprof-compute, rocprof-sys, Omniperf/Omnitrace) or NVIDIA (CUDA, Nsight Systems/Compute, NCU) profiling toolchains, with deep understanding of GPU architecture: warp/wavefront execution, memory hierarchy, occupancy, and instruction-level parallelism.
  • Experience analyzing GPU communication and networking performance including NCCL/RCCL, RDMA/RoCE, GPUDirect RDMA, UCX, MPI, ConnectX, Pensando, or similar high-performance networking technologies.
  • Experience with multi-GPU and multi-node inference, training, or HPC environments including tensor parallelism, pipeline parallelism, distributed communication libraries, and network performance analysis tools.
  • Experience with Linux systems performance analysis, operating systems, device drivers, virtualization, container runtimes, or low-level systems software development.
  • Demonstrated ability to explain performance differences in written reports or presentations-not just "X is faster" but why, rooted in hardware and software evidence.
  • Strong Python and C/C++ skills; comfort reading GPU kernel code (HIP/CUDA), runtime code, or systems-level software.
  • Experience with Kubernetes GPU scheduling, MIG, GPU operator performance, or contributions to open-source infrastructure, systems, networking, inference, or profiling projects.

ACADEMIC CREDENTIALS:
  • Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field preferred; advanced degree desired.

This role is not eligible for visa sponsorship.
#LI-TB1
#LI-Hybrid
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
This posting is for an existing vacancy.

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