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Gpu Performance Engineer Jobs in Michigan (NOW HIRING)

Utilidata

AI Infrastructure Engineer

Ann Arbor, MI · On-site +1

$170K - $210K/yr

Optimize GPU utilization and inference performance across our hardware fleet, including NVIDIA ... software engineering experience with a strong focus on AI infrastructure, backend systems, or ...

KLA Corporation

HPC Systems Engineer

Ann Arbor, MI · On-site

Our expert teams of physicists, engineers, data scientists and problem-solvers work together with ... and GPU resources • Diagnose performance bottlenecks across compute, storage, network, and ...

KLA Corporation

HPC Systems Engineer

Ann Arbor, MI

Our expert teams of physicists, engineers, data scientists and problem-solvers work together with ... GPU resources Diagnose performance bottlenecks across compute, storage, network, and scheduler ...

Orbotech

HPC Systems Engineer

Ann Arbor, MI · On-site

Our expert teams of physicists, engineers, data scientists and problem-solvers work together with ... GPU resources Diagnose performance bottlenecks across compute, storage, network, and scheduler ...

Kla

HPC Systems Engineer

Ann Arbor, MI

Our expert teams of physicists, engineers, data scientists and problem-solvers work together with ... GPU resources Diagnose performance bottlenecks across compute, storage, network, and scheduler ...

KLA Corporation

Senior AI Ops Engineer

Ann Arbor, MI · On-site

$102K - $140K/yr

Enable and optimize distributed GPU training (throughput, reliability, and cost), including ... performance incentive programs and eligibility for additional benefits including but not limited to ...

Orbotech

Senior AI Ops Engineer

Ann Arbor, MI · On-site

$102K - $140K/yr

Enable and optimize distributed GPU training (throughput, reliability, and cost), including ... performance incentive programs and eligibility for additional benefits including but not limited to ...

KLA Corporation

Senior AI Ops Engineer

Ann Arbor, MI

$102K - $140K/yr

Enable and optimize distributed GPU training (throughput, reliability, and cost), including ... performance incentive programs and eligibility for additional benefits including but not limited to ...

Kla

Senior AI Ops Engineer

Ann Arbor, MI

$102K - $140K/yr

Enable and optimize distributed GPU training (throughput, reliability, and cost), including ... performance incentive programs and eligibility for additional benefits including but not limited to ...

Deloitte

HPC AI Solution Architect (S2S)

Detroit, MI · On-site

... Engineering practice, you will design and drive deployment of fully integrated architectures for GPU-accelerated AI factories and high-performance computing infrastructure in close partnership with ...

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Gpu Performance Engineer information

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 Michigan? For Gpu Performance Engineer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Gpu Performance Engineer jobs in Michigan look for? The top searched job categories for Gpu Performance Engineer jobs in Michigan are:
What cities in Michigan are hiring for Gpu Performance Engineer jobs? Cities in Michigan with the most Gpu Performance Engineer job openings:
Gpu Performance Engineer jobs near you
AI Infrastructure Engineer

AI Infrastructure Engineer

Utilidata

Ann Arbor, MI • On-site, Remote

$170K - $210K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 3 days ago

Job description

Utilidata is a fast-growing NVIDIA-backed AI company enabling AI data centers to dynamically orchestrate power and unlock more compute capacity from existing energy infrastructure. For over a decade, we have applied AI to the electric grid - bringing real-time visibility and power-flow control to complex energy infrastructure. Our Karman platform, built on a custom NVIDIA module, brings that same capability to AI data centers, giving operators a way to better use the power already available to them.
The AI Infrastructure Engineer is responsible for designing, building, and owning the end-to-end infrastructure that serves Utilidata's AI and ML models across edge deployments, cloud environments, and data center integrations. They are also responsible for designing, building, and owning the integration of power data with AI inference software. This is Utilidata's first dedicated role of this kind, and will serve as the foundational function for how the company deploys and operates AI capabilities in production. The role requires deep technical expertise in ML model serving, distributed systems, and GPU infrastructure, with a strong emphasis on reliability, performance, and scalability. This position works cross-functionally with product, engineering, and data science teams and is open to fully remote candidates, with periodic travel expected for company retreats and key on-site engagements.
Responsibilities
  • Lead the design and build of Utilidata's AI inference platform - establishing architecture patterns, deployment standards, and operational practices that will scale with the company
  • Own end-to-end model serving infrastructure for Utilidata's AI infrastructure (on-prem and datacenter)
  • Build and maintain fault-tolerant, high-performance systems for serving AI models at scale, with a focus on low latency, reliability, and cost efficiency
  • Collaborate closely with algorithms engineers to integrate AI inference data and configuration with power optimization algorithms
  • Optimize GPU utilization and inference performance across our hardware fleet, including NVIDIA accelerators central to Utilidata's edge AI platform
  • Establish MLOps best practices including CI/CD pipelines for model deployment, monitoring, and rollback across environments
  • Contribute to infrastructure roadmap decisions, including build vs. buy tradeoffs, tooling selection, and platform evolution as the team grows

Minimum Qualifications
  • 5+ years of software engineering experience with a strong focus on AI infrastructure, backend systems, or distributed systems
  • Hands-on experience with AI model serving frameworks (e.g., vLLM, SGLang, Triton, TensorRT, TorchServe, or similar)
  • Understanding of container orchestration and cluster management (Kubernetes, Docker)
  • Experience deploying and operating infrastructure across both datacenter and on-prem environments
  • Strong knowledge of GPU workloads and the tradeoffs that come with them - you understand how inference differs from training, and why it matters
  • Proficiency in Python; C++, CUDA, Go, Rust a plus
  • Excellent communication skills and comfort working cross-functionally in a lean, fast-moving environment
  • Willingness to travel up to 10% of time

Enhanced Qualifications (Nice to Have)
  • Dynamo experience a plus
  • Experience with edge AI deployments or constrained compute environments
  • Familiarity with infrastructure as code (Terraform, Helm)
  • Experience with observability platforms (Datadog, Prometheus, Grafana)
  • Background in energy, utilities, or industrial IoT
  • Contributions to open-source ML infrastructure projects

Salary Range: $170,000 to $210,000 base compensation depending on experience plus stock options. Salary will be commensurate with an individual's skills, training, years of experience, and in line with internal compensation bands.
Location: This position can be performed remotely from anywhere in the United States.
Our Commitments:
Utilidata values the diversity of our team. We provide equal employment opportunities without regard to race, color, religion, creed, sex, gender, sexual orientation, gender identity or expression, national origin, age, physical disability, mental disability, medical condition, pregnancy or childbirth, sexual orientation, genetics, genetic information, marital status, or status as a covered veteran or any other basis protected by applicable federal, state and local laws.
We are committed to:
  • Creating a diverse and inclusive workplace that is welcoming, supportive, affirming and respectful
  • Empowering employees to solve problems and work together to make a difference
  • Providing mentorship and growth opportunities as part of a collaborative team
  • A flexible work environment with flexible paid time off
  • Competitive compensation and benefits, including health, dental, vision, and employer-match 401k

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