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Gpu Performance Engineer Jobs in New Mexico (NOW HIRING)

Senior AI Systems Engineer

Albuquerque, NM · On-site +1

$95K - $130K/yr

Maintain observability across AI systems through logging, metrics, performance monitoring, alerting ... Experience with GPU-based systems or running AI models in HPC environments. * Experience writing ...

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Gpu Performance Engineer information

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 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 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 New Mexico?

For Gpu Performance Engineer jobs in New Mexico, the most frequently searched job titles are:

What job categories do people searching Gpu Performance Engineer jobs in New Mexico look for?

The top searched job categories for Gpu Performance Engineer jobs in New Mexico are:

What cities in New Mexico are hiring for Gpu Performance Engineer jobs?

Cities in New Mexico with the most Gpu Performance Engineer job openings:

Principal Network Engineer - AI Infrastructure

Koitecc Solutions

Santa Fe, NM • On-site

$144.20 - $288.40/hr

Other

Medical, Dental, Vision, Retirement, PTO

Posted yesterday

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Job description

We're building a world of health around every individual - shaping a more connected, convenient and compassionate health experience. At CVS Health®, you'll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger - helping to simplify health care one person, one family and one community at a time.

Position Summary

The Principal Network Engineer - AI Infrastructure plays a key role in building the high‑performance network infrastructure that powers the organization's AI and GPU‑driven workloads. This position is responsible for designing and delivering scalable data center solutions that support large‑scale training and inference platforms. By leveraging modern architectures such as leaf‑spine fabrics, and aligning with leading vendor and industry reference designs, the role helps enable reliable, high‑throughput environments that directly support critical business initiatives.

Working closely with engineering, platform, and security partners, this role helps connect network, compute, and security capabilities into a cohesive, high‑performing ecosystem. In addition to hands‑on technical contribution, the position provides guidance on best practices, supports the development of other engineers, and helps shape the future direction of the organization's AI infrastructure. Through continuous improvement, thoughtful design, and a focus on performance and resilience, this role contributes to a secure and scalable foundation that supports long‑term growth and innovation.

Role Responsibilities Collaboration & Expertise
  • Partner with compute, storage, platform, and security teams to design integrated AI infrastructure solutions.
  • Serve as a senior technical authority aligning network designs with NVIDIA, Cisco, and industry reference architecture.
  • Influence enterprise network and security strategy through collaboration with engineering leadership and stakeholders.
Analysis & Configuration
  • Design and implement high-performance data center networks optimized for AI/GPU workloads, including leaf‑spine and EVPN/VXLAN fabrics.
  • Integrate networking with GPU clusters and high-performance storage systems supporting training and inference workloads.
  • Optimize network performance (latency, throughput, congestion) for large-scale distributed environments.
  • Evaluate and deploy advanced networking technologies to improve scalability, reliability, and security.
Operational Support
  • Support 24/7 infrastructure operations, including on‑call responsibilities across cloud, on‑prem, and colocation environments.
  • Lead incident response and resolution for network‑related issues, driving root cause analysis and resilience improvements.
Mentorship and Training
  • Mentor and develop engineers, promoting best practices in networking and security.
  • Support knowledge sharing through training sessions and technical enablement.
Innovation and Research
  • Evaluate and adopt emerging AI infrastructure and networking technologies (e.g., high‑speed interconnects, next‑gen switching).
  • Contribute to research, innovation, and continuous improvement of network and security capabilities.
Strategic Planning
  • Define and drive the data center network strategy supporting AI/ML platforms and business initiatives.
  • Establish standards and reference architecture aligned with industry best practices.
  • Guide long‑term roadmap decisions, balancing performance, scalability, security, and risk.
Required Qualifications
  • 10+ years of experience in network engineering, with at least 5+ years in a leadership, architectural, or lead engineering role delivering enterprise or cloud network initiatives end‑to‑end.
  • 5+ years of experience designing and operating large‑scale data center networks, including Layer 2/3 architectures (leaf‑spine/Clos), EVPN/VXLAN overlays, and high‑speed networking (100/200/400Gb+).
  • 5+ years of experience with enterprise routing, switching, and network platforms, including Cisco‑centric data center fabrics, protocols (BGP, OSPF, MPLS, STP), and hybrid connectivity (SD‑WAN, VPN, remote access).
  • 5+ years of experience implementing network security technologies, including Palo Alto Networks firewalls (required), NGFW, IDS/IPS, ZTNA, DLP, and micro‑segmentation, with understanding of application‑aware and zero trust architectures.
  • 3+ years of experience supporting AI/ML or GPU‑based environments, including NVIDIA reference architectures and performance‑optimized networking for distributed training workloads (e.g., traffic flow optimization, congestion management).
  • 3+ years of experience with application delivery and observability technologies, including F5 load balancing, network performance monitoring tools (e.g., NetFlow, Wireshark, SolarWinds), and traffic analysis for performance tuning.
Preferred Qualifications
  • Experience designing and supporting AI factory / GPU cluster environments at scale (training and inference platforms).
  • Familiarity with high‑performance compute networking enhancements (RDMA over Converged Ethernet - RoCE, PFC, ECN).
  • Experience with Cisco Nexus, ACI, or equivalent data center switching platforms supporting AI workloads.
  • Strong technical expertise with Networking and Software‑Defined Networking (SDN) principles.
  • Strong technical expertise with developing and interpreting Network, Sequence, and Dataflow diagrams.
  • Understanding of at least one compliance framework (HIPAA, HITRUST, PCI, NIST, CSA).
  • Strong technical expertise in defining and implementing cyber resilience standards, policies, and programs for distributed cloud and network infrastructure, ensuring robust redundancy and system reliability.
  • Experience in influencing industry standards and contributing to open‑source projects or security communities, highlighting a broader impact beyond the immediate organizations.
  • Experience with network automation and Infrastructure as Code.
  • Background in high‑availability and disaster recovery design.
  • Certifications: CCIE/CCNP, JNCIE, AWS/Azure/GCP Networking, PCNSE/PAN or Security Specialty, CISSP.
Education
  • Bachelor's degree or equivalent experience (High School Diploma and 4 years relevant experience).
Pay Range

The typical pay range for this role is:
$144,200.00 - $288,400.00
This pay range represents the base hourly rate or base annual full‑time salary for all positions in the job grade within which this position falls. The actual base salary offer will depend on a variety of factors including experience, education, geography and other relevant factors. This position is eligible for a CVS Health bonus, commission or short‑term incentive program in addition to the base pay range listed above. This position also includes an award target in the company’s equity award program.

Our people fuel our future. Our teams reflect the customers, patients, members and communities we serve and we are committed to fostering a workplace where every colleague feels valued and that they belong.

Great benefits for great people

We take pride in offering a comprehensive and competitive mix of pay and benefits that reflects our commitment to our colleagues and their families.

This full‑time position is eligible for a comprehensive benefits package designed to support the physical, emotional, and financial well‑being of colleagues and their families. The benefits for this position include medical, dental, and vision coverage, paid time off, retirement savings options, wellness programs, and other resources, based on eligibility.

Additional details about available benefits are provided during the application process and on Benefits Moments.

We anticipate the application window for this opening will close on: 07/27/2026

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state and local laws.

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