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Ai Chip Jobs (NOW HIRING)

Cerebras Systems

ASIC Architect

Sunnyvale, CA · On-site

$196K/yr

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than ...

Etched

Machine Learning Research Engineer

Cupertino, CA · On-site

$252K/yr

Etched is an AI chip startup that designs and manufactures hardware systems optimized for artificial intelligence model inference workloads. Founded in 2022, the company is headquartered in San Jose ...

Cerebras Systems

Electrical Engineer

Sunnyvale, CA · On-site

$150K - $260K/yr

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than ...

Nvidia

Manager, ASIC Chip - GPU

Santa Clara, CA · On-site

Join NVIDIA's powerful GPU ASIC team and drive the innovation of future GPU processors for AI ... Our team is at the forefront of modern technology, and we are looking for an ambitious ASIC Chip ...

CEREBRAS SYSTEMS INC.

Security SWE

Canada, KY · On-site

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than ...

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Ai Chip information

What is an AI chip?

AI chips are specialized hardware components designed to accelerate artificial intelligence workloads, such as machine learning and deep learning tasks. Unlike traditional CPUs, AI chips are optimized for processing large volumes of data in parallel, making them highly efficient for neural network computations. These chips are commonly used in data centers, smartphones, autonomous vehicles, and edge devices to enable faster and more energy-efficient AI processing.

What are some common challenges faced by professionals working in AI chip development, and how can they be addressed?

Professionals in AI chip development often encounter challenges such as balancing high computational performance with power efficiency, keeping up with rapid technological advancements, and integrating hardware with evolving AI algorithms. Collaboration between hardware engineers, software developers, and data scientists is essential to ensure that chips meet both performance and functional requirements. Staying current through ongoing learning and participating in cross-functional teams can help address these challenges and contribute to successful AI chip projects.

What are the key skills and qualifications needed to thrive as an AI chip engineer, and why are they important?

To thrive as an AI Chip Engineer, you need a solid background in electrical engineering, computer architecture, and experience with hardware design, typically supported by a relevant degree. Familiarity with hardware description languages (such as Verilog or VHDL), EDA tools, and knowledge of semiconductor fabrication processes are crucial technical requirements. Attention to detail, strong problem-solving abilities, and effective teamwork skills help you excel in complex project environments. These competencies are vital for developing high-performance, efficient AI chips that power modern artificial intelligence applications.

What is the difference between Ai Chip vs AI Hardware Engineer?

AspectAi ChipAI Hardware Engineer
Required CredentialsBachelor's or higher in Electrical Engineering, Computer Engineering, or related fields; knowledge of VLSI designBachelor's or higher in Electrical Engineering, Computer Engineering, or related fields; experience with hardware design and testing
Work EnvironmentDesign labs, manufacturing facilities, R&D centersDesign labs, testing facilities, R&D centers
Employer & Industry UsageTech companies, semiconductor firms, AI hardware startupsTech companies, semiconductor companies, research institutions

While both roles involve hardware and AI technology, an Ai Chip focuses on designing and developing AI-specific chips, whereas an AI Hardware Engineer works on the broader hardware systems that support AI applications, including integration and testing.

Which AI chip job is high paying?

Senior AI chip design engineers and hardware architects typically earn the highest salaries in AI chip jobs due to their specialized skills in semiconductor design, deep learning hardware, and system integration. These roles often require advanced degrees and experience with tools like FPGA or ASIC development, and they can command six-figure salaries depending on the company and location.

What cities are hiring for Ai Chip jobs?

Cities with the most Ai Chip job openings:

What states have the most Ai Chip jobs?

States with the most job openings for Ai Chip jobs include:

What job categories do people searching Ai Chip jobs look for?

The top searched job categories for Ai Chip jobs are:

Ai Chip jobs near you
Infographic showing various Ai Chip job openings in the United States as of August 2026, with employment types broken down into 5% Internship, 85% Full Time, and 10% Contract. Highlights an 95% In-person, and 5% Remote job distribution.

ASIC Architect

Cerebras Systems

Sunnyvale, CA • On-site

$196K/yr

Full-time

Re-posted 29 days ago

Job description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Responsibilities
  • Translate high level architecture spec to micro-architecture feature requirements
  • Bring up new features in the performance/power model
  • Perform comprehensive PPA trade-offs for new architectural features
  • Extract insights for new features and micro-architecture power efficiency
  • Profile workloads, identify bottlenecks and project competition performance for benchmarking
  • Engage with SW teams for end-end application level modeling at cluster level
  • Identify kernel level HW acceleration level opportunities

Qualifications
  • Masters/PhD in Electrical/Computer Engineering
  • 10+ years of experience across performance analysis and modeling across GPUs, CPUs or accelerator products
  • Strong background in computer architecture and key high level architectural trade-offs
  • Comfortable standing up new performance models from scratch in Python or similar analytical environments
  • Exposure to micro-code (kernel) performance bottlenecks and optimization techniques
  • Good understanding of how high-level workloads map to underlying micro-architecture is desired
  • Understanding of basic ML workload profiling techniques and model network architecture is preferred

Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we've reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
  1. Build a breakthrough AI platform beyond the constraints of the GPU.
  2. Publish and open source their cutting-edge AI research.
  3. Work on one of the fastest AI supercomputers in the world.
  4. Enjoy job stability with startup vitality.
  5. Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here!
Apply today and become part of the forefront of groundbreaking advancements in AI!
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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