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Gpu Performance Engineer Jobs (NOW HIRING)

Genmo

GPU Performance Engineer

San Francisco, CA · On-site

We're seeking a GPU Performance Engineer to squeeze every last FLOP from our H100 infrastructure and optimize our model serving stack to its absolute limits. The Role You'll be our performance ...

Genmo

GPU Performance Engineer

San Francisco, CA · On-site

We're seeking a GPU Performance Engineer to squeeze every last FLOP from our H100 infrastructure and optimize our model serving stack to its absolute limits. The Role You'll be our performance ...

SwiftCruit

Staff, GPU Performance Engineer

San Jose, CA · On-site

$167.80 - $251.80/hr

Role and Responsibilities We are seeking a highly skilled GPU Performance Engineer to join our team, responsible for workload analysis, benchmarking, and performance optimization of our GPU ...

Jobtailor

GPU Performance Engineer

Folsom, CA · On-site

$141.91 - $269.10/hr

As part of Intel's GPU performance team, the candidate will be working on Intel's new Graphics ... You will be working with a group of HW and SW architects, designers/developers in a multi ...

Anthropic

Performance Engineer, GPU

San Francisco, CA · On-site

$280K - $850K/yr

As a GPU Performance Engineer, you'll architect and implement the foundational systems that power Claude and push the frontiers of what's possible with large language models. You'll be responsible ...

Nvidia

OR

We are now looking for a GPU Performance Engineer for Neural Reconstruction! NVIDIA is building the future of computer graphics, simulation, robotics, and embodied AI. Neural reconstruction and ...

Nvidia

We are now looking for a GPU Performance Engineer for Neural Reconstruction! NVIDIA is building the future of computer graphics, simulation, robotics, and embodied AI. Neural reconstruction and ...

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Gpu Performance Engineer information

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$11

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How much do gpu performance engineer jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for gpu performance engineer in the United States is $60.11, according to ZipRecruiter salary data. Most workers in this role earn between $49.28 and $68.03 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.

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Gpu Performance Engineer jobs near you
Infographic showing various Gpu Performance Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 10% Part Time, and 2% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $125,019 per year, or $60.1 per hour.

GPU Performance Engineer

Two Sigma Investments, LP

New York, NY • On-site

$165K - $300K/yr

Full-time

Medical, Dental, Life, Retirement, PTO

Re-posted 4 days ago

Job description

GPU Performance Engineer
Location
NY New York
United States
Business
Investment Management
Function
Engineering
Experience Level
Experienced
Share this job
Position Summary
Two Sigma is a leading quantitative investment management and trading firm. The company applies a scientific approach to investing, combining cutting-edge technology, artificial intelligence, data science, and quantitative research with rigorous human inquiry to capitalize on market opportunities and deliver alpha for investors.
Our team of engineers, quantitative researchers and data scientists looks beyond the traditional to test hypotheses and develop creative solutions to some of the world's most complex economic problems.
Two Sigma is building a new team to drive the firm's strategic transition from CPU-centric to GPU-accelerated computation. Accelerated Compute sits within AI Innovation and operates at the intersection of quantitative modeling workflows, GPU performance engineering, and infrastructure strategy.
You are a GPU programming expert. You write CUDA, you optimize kernels, you understand the memory hierarchy, you know why naive GPU code is slow and how to make it fast. You will ensure that when workloads move to GPU, they achieve the performance that justifies the transition.
You will take on the following responsibilities:
  • Design and implement GPU-accelerated kernels for financial computation workloads
  • Optimize GPU code for throughput, latency, and memory efficiency across current and next-generation hardware (Blackwell, Rubin)
  • Develop procedures for precision management (FP8/FP4 training and inference) in financial applications
  • Profile and optimize GPU workloads using NVIDIA tooling (Nsight Systems, Nsight Compute)
  • Build reusable GPU libraries and abstractions that modeling teams can use without requiring deep CUDA expertise
  • Evaluate and integrate GPU-accelerated libraries (RAPIDS, CUTLASS, cuBLAS, TensorRT) for financial use cases

You should possess the following qualifications:
  • BS or MS in Science, Technology, Engineering or Math
  • Minimum 1 year of experience required; 4-10 years of experience preferred
  • Expert-level CUDA programming: kernel development, memory management, stream and graph optimization
  • Deep understanding of GPU architecture: SM structure, warp scheduling, memory hierarchy (registers, shared memory, L1/L2, HBM)
  • Experience with performance profiling and optimization of GPU workloads
  • Strong C++ and Python skills, as well as familiarity with mixed-precision computation and numerical stability
  • Track record of delivering meaningful speedups on real workloads (not just benchmarks)

Preferred experience:
  • Background in HPC, scientific computing, or computational finance
  • Experience with multi-GPU and multi-node GPU programming (NCCL, MPI)
  • Familiarity with GPU-accelerated data processing frameworks (RAPIDS, cuDF)

You will enjoy the following benefits:
  • Core Benefits: Fully paid medical and dental insurance premiums for employees and dependents, competitive 401k match, employer-paid life & disability insurance
  • Perks: Onsite gyms with laundry service, wellness activities, casual dress, snacks, game rooms
  • Learning: Tuition reimbursement, conference and training sponsorship
  • Time Off: Generous vacation and unlimited sick days, competitive paid caregiver leaves
  • Hybrid Work Policy: Flexible in-office days with budget for home office setup

The base pay for this role will be between $165,000 and $300,000. This role may also be eligible for other forms of compensation and benefits, such as a discretionary bonus, health, dental and other wellness plans and 401(k) contributions. Discretionary bonus can be a significant portion of total compensation. Actual compensation for successful candidates will be carefully determined based on a number of factors, including their skills, qualifications and experience.
We are proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.
Two Sigma is committed to providing reasonable accommodations to qualified individuals in accordance with applicable federal, state, and local laws.
If you believe you need an accommodation, please visit our website for additional information.

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