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Gpu Jobs in Michigan (NOW HIRING)

Zoetis, Inc.

HPC Platform Product Lead

Kalamazoo, MI · On-site

Linux administration fundamentals, compute (CPU/GPU), high-speed networking, shared/parallel storage concepts, and capacity/performance planning. * Automation/scripting (e.g., Bash/Python) and ...

Abaxis

HPC Platform Product Lead

Portage, MI · On-site +1

Linux administration fundamentals, compute (CPU/GPU), high-speed networking, shared/parallel storage concepts, and capacity/performance planning. * Automation/scripting (e.g., Bash/Python) and ...

Bosch Group

Technical Lead- AI Cockpit

Plymouth, MI · On-site

Architect efficient edge AI execution, optimizing models for CPU/GPU/NPU (quantization, pruning, latency, power) * Design hybrid edge-cloud architectures for personalization, continuous learning, and ...

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$12

$47

$62

How much do gpu jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for gpu in Michigan is $47.89, according to ZipRecruiter salary data. Most workers in this role earn between $47.16 and $56.59 per hour, depending on experience, location, and employer.

What is a GPU?

A GPU, or Graphics Processing Unit, is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images and graphics for display. While originally developed for rendering graphics in video games and visual applications, GPUs are now widely used for parallel processing tasks in areas such as artificial intelligence, data science, and scientific computing. Their architecture allows them to handle thousands of operations simultaneously, making them much faster than traditional CPUs for certain workloads.

What is a GPU engineer?

A GPU job refers to a computing task that utilizes a Graphics Processing Unit (GPU) for acceleration. GPUs are specialized processors designed for parallel processing, making them ideal for tasks like machine learning, scientific simulations, and rendering. Many software applications offload intensive computations to GPUs to improve performance and efficiency. Jobs related to GPUs can involve programming, optimization, and hardware configuration in fields like AI, gaming, and data analysis.

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

To thrive as a GPU Engineer, you need a solid background in computer engineering, mathematics, and programming languages such as C++ or CUDA, often supported by a relevant degree. Familiarity with GPU architectures, parallel computing frameworks, and tools like OpenCL or Vulkan is typically required. Analytical thinking, problem-solving, and teamwork are essential soft skills for innovating and debugging complex systems. These abilities are crucial for optimizing performance, ensuring compatibility, and driving advancements in graphics and computational workloads.

What are some common challenges faced by GPU engineers when optimizing performance for various applications?

GPU engineers often encounter challenges such as balancing high computational throughput with power efficiency, ensuring compatibility across different hardware architectures, and optimizing code for parallel processing. They must also troubleshoot bottlenecks in memory bandwidth and latency that can impact performance. Collaboration with software developers and hardware architects is crucial to identify and resolve these issues, and staying updated with the latest advances in GPU technologies is essential for continued success.

What is the difference between Gpu vs Data Scientist?

AspectGpuData Scientist
Required CredentialsKnowledge of parallel computing, programming skills (CUDA, OpenCL)Degree in Computer Science, Statistics, or related fields; programming skills
Work EnvironmentHardware-focused, technical, often in R&D or engineering teamsData analysis, modeling, research in various industries
Industry UsageTech, gaming, AI, machine learningFinance, healthcare, tech, marketing

Gpu specialists focus on hardware and parallel processing for computing tasks, while data scientists analyze data to extract insights. Both roles require technical skills, but Gpu roles are more hardware-oriented, whereas data scientists focus on data analysis and modeling.

How to get into the graphics processing unit industry?

To enter the GPU industry, candidates typically need a strong background in computer engineering, electrical engineering, or computer science, with skills in programming languages like C++ and knowledge of graphics APIs such as DirectX or Vulkan. Relevant experience can be gained through internships, projects, or certifications in hardware design, GPU architecture, or related fields. Staying updated on industry developments and obtaining certifications like NVIDIA's or AMD's developer programs can also enhance job prospects.

What are the most commonly searched types of Gpu jobs in Michigan?

The most popular types of Gpu jobs in Michigan are:

What are popular job titles related to Gpu jobs in Michigan?

For Gpu jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Gpu jobs in Michigan look for?

The top searched job categories for Gpu jobs in Michigan are:

What cities in Michigan are hiring for Gpu jobs?

Cities in Michigan with the most Gpu job openings:

Gpu jobs near you
Infographic showing various Gpu job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 11% Part Time, and 2% Contract. Highlights an 77% Physical, 7% Hybrid, and 16% Remote job distribution, with an average salary of $99,607 per year, or $47.9 per hour.

Senior Machine Learning Engineer (Computer Vision & AI)

faurecia S.A.

Auburn Hills, MI • On-site

$100 - $125/hr

Other

Posted 20 days ago

Job description

Forvia, a sustainable mobility technology leaderRole Summary

As a Senior Machine Learning Engineer, you will play a key role in developing and deploying advanced AI solutions that drive innovation across our automotive products and manufacturing processes. This role is focused on applied machine learning, with an emphasis on computer vision, predictive analytics, and scalable model deployment. You will partner with cross‑functional teams across engineering, manufacturing, and R&D to design, build, and productionize machine learning models that deliver measurable business impact. This is a hands‑on role requiring strong technical depth, ownership of model lifecycle, and the ability to operate effectively in a fast‑paced, evolving environment.

Key Responsibilities
  • Design, develop and deploy machine learning models with a focus on computer vision, predictive maintenance and anomaly detection
  • Build and maintain end‑to‑end ML pipelines, including data preprocessing, model training, evaluation and deployment
  • Collaborate with engineering, manufacturing and business stakeholders to translate real‑world problems into scalable AI solutions
  • Optimize model performance and scalability using GPU acceleration and parallel computing frameworks
  • Leverage Python and deep learning frameworks (PyTorch, TensorFlow) to implement production‑ready solutions
  • Partner with data and software engineering teams to integrate models into production systems and workflows
  • Contribute to AI innovation initiatives, including exploration of generative AI use cases in design and engineering contexts
  • Ensure best practices in model reproducibility, documentation and performance monitoring
  • Stay current with advancements in machine learning and apply relevant techniques to improve existing capabilities
Your Profile & Competencies
  • Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field
  • 5+ years (preferably 7+) of hands‑on experience in machine learning or applied AI roles
  • Strong expertise in machine learning and deep learning, with practical experience in computer vision applications
  • Proficiency in Python and modern ML frameworks such as PyTorch or TensorFlow
  • Experience building and deploying models in production environments
  • Familiarity with GPU acceleration (CUDA or similar frameworks) and performance optimization techniques
  • Experience with ML pipelines, model lifecycle management and scalable system design
  • Ability to work effectively in matrixed, cross‑functional environments
  • Strong problem‑solving and analytical skills, with the ability to translate complex data into actionable insights
  • Effective communicator, able to explain technical concepts to non‑technical stakeholders
  • Experience in automotive, manufacturing or industrial environments is a strong plus
  • Familiarity with cloud platforms (e.g., Azure) and Agile development practices is preferred
What we can do for you
  • We provide an engaging and dynamic environment where you can contribute to the development of sustainable mobility leading technologies.
  • Our company is the seventh‑largest global automotive supplier, offering ample opportunities for career development.
  • We welcome energetic and agile people who can thrive in a fast‑changing environment and share our strong values.
  • We cultivate a learning environment, dedicating tools and resources to ensure we remain at the forefront of mobility. Our people enjoy an average of more than 22 hours of online and in‑person training within FORVIA University.
  • We offer a multicultural environment that values diversity and international collaboration and have adopted gender diversity targets and inclusion action plans.
  • We are committed to achieving CO2 Net Zero. In June2022, Forvia became the first global automotive group to be certified with the new SBTI Net‑Zero Standard.
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