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Gpu Performance Engineer Jobs in Colorado (NOW HIRING)

HPC Cloud Performance Engineer

Aurora, CO · On-site

$56.75 - $76/hr

HPC Cloud Performance Engineer LOCATION Aurora, CO 80014 CLEARANCE TS/SCI Full Poly (Please note ... Knowledge of GPU acceleration * Familiarity with containerization (e.g., Docker, Singularity)

HPC Cloud Performance Engineer

Aurora, CO · On-site

$56.75 - $76/hr

HPC Cloud Performance Engineer LOCATIONAurora, CO 80014 CLEARANCETS/SCI Full Poly (Please note this ... Knowledge of GPU acceleration * Familiarity with containerization (e.g., Docker, Singularity)

An era in which our tightly coupled CPU, GPU and DPU technology acts as the brains of computers ... analyze performance What we need to see: * BS or MS degree in Computer Engineering, Computer ...

... to optimize compute/GPU performance and eliminate thermal throttling of any/all compute ... Support the engineering transition from air-cooled to liquid-cooled (DLC/CDU) architectures ...

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Gpu Performance Engineer information

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 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 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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Machine Learning Performance Engineer

Machine Learning Performance Engineer

Keysight Technologies, Inc.

Loveland, CO

$160.16K - $266.93K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 11 days ago


Keysight Technologies rating

7.6

Company rating: 7.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

66th of 137 rated electronics manufacturers


Job description

Overview

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

The AI Models and Data Science team at Keysight AI Labs is hiring a ML Performance Engineer to make our training and inference stacks as fast as the math allows. You'll own end-to-end performance: profiling training workloads on multi-GPU clusters, writing custom CUDA kernels and LibTorch C++ extensions for hot paths, and optimizing inference for embedding in production software where every millisecond matters.

This role sits at the intersection of ML, systems engineering, and HPC. You'll work directly with MLEs and data scientists driving the modeling work, and with the engineering teams shipping these models into Keysight products.


Responsibilities
  • Profile and optimize training workloads — multi-GPU scaling efficiency, throughput, memory footprint, mixed precision, gradient checkpointing tradeoffs
  • Profile and optimize inference for low-latency, high-throughput deployment — quantization, graph optimization, kernel fusion, runtime selection
  • Write custom CUDA kernels and LibTorch (PyTorch C++) extensions to accelerate hot paths in both training and inference
  • Build and maintain serving infrastructure using ONNX Runtime, TensorRT, and similar — including C++ integration paths for embedding models inside production software
  • Partner with MLEs and data scientists on perf-aware architecture choices; partner with product engineering on deployment, versioning, and monitoring
  • Establish performance SLAs and regression tests so models stay fast as they evolve

Qualifications
  • 4+ years in ML engineering, performance engineering, or HPC, with substantial production ML experience
  • Strong Python and C++ — including LibTorch / PyTorch C++ extensions in production
  • Hands-on experience optimizing both training and inference workloads (not just one)
  • CUDA experience required — comfortable profiling GPU code with Nsight and reasoning about occupancy, memory hierarchy, and kernel-level tradeoffs
  • Production deployment experience with ONNX Runtime, TensorRT, or equivalent inference runtimes
  • Solid software engineering fundamentals: testing, versioning, code review, monitoring
  • Experience with Docker and container-based deployment

Careers Privacy Statement
Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

The level of role and salary will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.

California Pay Range: MIN $160,160- MAX $266,930

Note: For other locations, pay ranges will vary by region.

US Employees may be eligible for the following benefits:

- Medical, dental and vision

- Health Savings Account

- Health Care and Dependent Care Flexible Spending Accounts

- Life, Accident, Disability insurance

- Business Travel Accident and Business Travel Health

- 401(k) Plan

- Flexible Time Off, Paid Holidays

- Paid Family Leave

- Discounts, Perks

- Tuition Reimbursement

- Adoption Assistance

- ESPP (Employee Stock Purchase Plan)

Qualifications:
  • 4+ years in ML engineering, performance engineering, or HPC, with substantial production ML experience
  • Strong Python and C++ — including LibTorch / PyTorch C++ extensions in production
  • Hands-on experience optimizing both training and inference workloads (not just one)
  • CUDA experience required — comfortable profiling GPU code with Nsight and reasoning about occupancy, memory hierarchy, and kernel-level tradeoffs
  • Production deployment experience with ONNX Runtime, TensorRT, or equivalent inference runtimes
  • Solid software engineering fundamentals: testing, versioning, code review, monitoring
  • Experience with Docker and container-based deployment

Careers Privacy Statement
Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

The level of role and salary will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.

California Pay Range: MIN $160,160- MAX $266,930

Note: For other locations, pay ranges will vary by region.

US Employees may be eligible for the following benefits:

- Medical, dental and vision

- Health Savings Account

- Health Care and Dependent Care Flexible Spending Accounts

- Life, Accident, Disability insurance

- Business Travel Accident and Business Travel Health

- 401(k) Plan

- Flexible Time Off, Paid Holidays

- Paid Family Leave

- Discounts, Perks

- Tuition Reimbursement

- Adoption Assistance

- ESPP (Employee Stock Purchase Plan)

Education:UNAVAILABLEEmployment Type: UNAVAILABLE

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