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Ai Chips Jobs (NOW HIRING)

Etched

Firmware Engineer

Cupertino, CA · On-site

Our ASIC systems deliver orders of magnitude higher performance than conventional AI chips. Etched is actively developing Sohu, an ASIC exclusively for transformers, which will enable products that ...

Etched

RTL Design Engineer

San Jose, CA · On-site

$150K - $275K/yr

Job Summary As an RTL Engineer at Etched, you will be critical in ensuring that our AI chips operate correctly and efficiently. You will develop and implement design verification strategies for both ...

Etched

Machine Learning Research Engineer

Cupertino, CA · On-site

$252K/yr

Etched is building AI chips that are hard-coded for individual model architectures. The Machine Learning Research Engineer will propose and conduct research to optimize performance on Sohu ...

Etched

RTL Design Engineer

San Jose, CA · On-site

$150K - $275K/yr

Job Summary As an RTL Engineer at Etched, you will be critical in ensuring that our AI chips operate correctly and efficiently. You will develop and implement design verification strategies for both ...

Etched

Performance Modeling Engineer

Cupertino, CA · On-site

Our ASIC systems deliver orders of magnitude higher performance than conventional AI chips. Etched is actively developing Sohu, an ASIC exclusively for transformers, which will enable products that ...

Etched

Performance Modeling Engineer

Cupertino, CA

Our ASIC systems deliver orders of magnitude higher performance than conventional AI chips. Etched is actively developing Sohu, an ASIC exclusively for transformers, which will enable products that ...

Etched

Inference Software Engineer

Cupertino, CA

$2.0K/mo

About Etched Etched is building AI chips that are hard-coded for individual model architectures. Our first product (Sohu) only supports transformers, but has an order of magnitude more throughput and ...

Etched

Performance Modeling Engineer

Cupertino, CA · On-site

Our ASIC systems deliver orders of magnitude higher performance than conventional AI chips. Etched is actively developing Sohu, an ASIC exclusively for transformers, which will enable products that ...

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Ai Chips information

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How much do ai chips jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for ai chips in the United States is $14.83, according to ZipRecruiter salary data. Most workers in this role earn between $10.82 and $15.38 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working with AI chips, and how can they be addressed?

Working with AI chips often involves navigating rapidly evolving hardware architectures and keeping pace with new advancements. Professionals may face challenges related to optimizing software for specific chip designs, ensuring compatibility across platforms, and addressing power and thermal limitations. Collaboration with cross-functional teams—such as data scientists, hardware engineers, and software developers—is crucial to successfully integrate AI chips into products. Staying updated through continuous learning and leveraging available documentation and development tools can help mitigate these challenges.

What are AI chips?

AI chips are specialized hardware components designed to efficiently process artificial intelligence workloads, such as machine learning and deep learning tasks. Unlike traditional CPUs, AI chips (like GPUs, TPUs, and NPUs) are optimized for parallel processing and high-speed calculations required by AI algorithms. They help accelerate training and inference in applications ranging from speech recognition to autonomous vehicles. As AI technology advances, AI chips are becoming increasingly important in data centers, edge devices, and consumer electronics.

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 strong background in electrical engineering, computer architecture, and semiconductor design, typically supported by a relevant degree. Proficiency with hardware description languages (such as Verilog or VHDL), EDA tools (like Cadence or Synopsys), and experience with high-level AI frameworks are commonly required. Strong problem-solving skills, attention to detail, and effective teamwork are critical soft skills for excelling in this role. These skills and qualities are essential to design high-performance, efficient AI chips that meet the complex demands of modern AI applications.

What is the difference between Ai Chips vs Data Scientists?

AspectAi ChipsData Scientists
Required CredentialsKnowledge of hardware design, electrical engineering, sometimes certifications in chip designDegree in data science, statistics, computer science; certifications like Certified Analytics Professional
Work EnvironmentHardware labs, R&D facilities, manufacturing plantsOffice settings, research labs, data analysis environments
Industry UsageTechnology, semiconductor manufacturing, AI hardware developmentTech companies, finance, healthcare, research institutions

Ai Chips focus on designing and developing specialized hardware for AI applications, requiring technical hardware skills. Data Scientists analyze data to extract insights, requiring statistical and programming expertise. While both roles are integral to AI development, they differ significantly in skills, environment, and industry focus.

Infographic showing various Ai Chips job openings in the United States as of August 2026, with employment types broken down into 5% Internship, 79% Full Time, and 16% Contract. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $30,839 per year, or $14.8 per hour.

AI Infrastructure Engineer - Emerging Technologies

Cologix

Ashburn, VA • Remote

$110K - $144K/yr

Full-time

Re-posted 28 days ago

Job description

About Our Company:
Headquartered in Denver, Colorado, Cologix is a leading North America network-neutral interconnection and hyperscale edge data center company. Our platform gives customers access to 45+ digital edge and Scalelogix℠ hyperscale edge data centers in 13 markets across the United States and Canada along with a carrier-dense ecosystem of 710+ networks, 360+ cloud providers, 35+ onramps and seven Internet exchanges. We provide our nearly 2,000 customers with direct access to our local operations teams, resulting in strong partnerships enabled by exceptional operational support and unparalleled customer service. Backed by one of the largest North American infrastructure funds, Cologix's experienced leadership team, certified staff and commitment to ESG initiatives help form a culture that values our people, our environment and our clients.

About the Position:

The AI Infrastructure Engineer – Emerging Technologies will support the Office of the VP of Technology Engineering & Innovation in evaluating, designing, and developing next-generation AI-ready data center infrastructure strategies.

This role serves as a bridge between emerging AI technologies and practical implementation across data center development, engineering, construction, operations, and energy infrastructure planning.

The position will focus on assessing how rapidly evolving AI compute architectures, high-density rack deployments, advanced cooling systems, and emerging power technologies will impact future data center design standards, development strategies, construction methodologies, and operational models.

The ideal candidate combines expertise in AI infrastructure, power systems, cooling technologies, and emerging compute platforms with the ability to translate industry trends into actionable engineering and infrastructure strategies for future AI-enabled data center environments.

This role is ideal for someone who is highly analytical, technically curious, and capable of bridging emerging AI compute trends with real-world infrastructure strategy and execution. This individual should be comfortable operating across engineering, operations, construction, energy strategy, and innovation functions while helping shape the future direction of AI-enabled data center development.

This role will help evaluate and guide the following areas:

  • Future AI rack density and power consumption trends
  • Impacts of next-generation GPU and AI chip architectures
  • Optical networking and switching implications on infrastructure design
  • AI workload impacts on utility infrastructure and power quality
  • Evolution of liquid cooling and high-density thermal management
  • Grid-parallel and microgrid strategies for AI campuses
  • Future AI-ready development and construction standards
  • Long-term AI infrastructure innovation roadmaps
  • Vendor technology evaluation and infrastructure modernization strategies
What you do daily:

AI Infrastructure Strategy & Analysis

  • Support the VP of Technology Engineering & Innovation in evaluating emerging AI infrastructure technologies and future-ready data center strategies.
  • Analyze AI workload characteristics including:
  • Training vs. inference workloads
  • GPU utilization patterns
  • Dynamic workload fluctuations
  • Rack-level power variability
  • Networking and latency requirements
  • Assess implications of AI workload behavior on infrastructure resiliency, scalability, efficiency, and operational design.
  • Develop technical recommendations and infrastructure strategies supporting future AI deployments.

AI Compute & Chip Architecture Evaluation

  • Analyze current and future AI compute platforms including NVIDIA GPU architectures, ARM-based platforms, custom AI accelerators and ASICs, optical networking and switching technologies, and emerging hyperscaler-designed AI chips.
  • Evaluate implications of evolving chip architectures on rack density, power consumption, cooling requirements, electrical distribution, mechanical infrastructure, space planning, and future development standards.
  • Model current and future AI rack power density trends including existing high-density deployments (50–120 kW), near-term AI deployments (150–300+ kW), and future ultra-dense AI cluster scenarios.
  • Assess long-term impacts of emerging chip architectures on energy efficiency and future data center design and development standards.

Data Center Design, Development & Construction

  • Support conceptual and detailed design efforts for AI-ready data center infrastructure.
  • Assist in developing long-term infrastructure roadmaps for high-density AI deployments, liquid cooling adoption, modular infrastructure strategies, utility coordination, grid-parallel and microgrid solutions, and future AI campus development.
  • Evaluate implications of AI infrastructure evolution on greenfield developments, existing facility retrofits, construction methodologies, scalability, and future campus master planning.
  • Collaborate with engineering, development, and construction teams to develop scalable AI-ready infrastructure standards and deployment models.

Energy Strategy & Power Infrastructure

  • Collaborate closely with the Energy Strategy Team to evaluate utility constraints, interconnection requirements, grid limitations, dynamic load fluctuation impacts, power quality and resiliency considerations, and onsite generation and distributed energy solutions.
  • Support analysis of grid-parallel and islanded microgrid architectures, fuel cells, Battery Energy Storage Systems (BESS), bridge power solutions, natural gas generation, and renewable integration opportunities.
  • Evaluate implications of AI workloads on substation development, transmission planning, utility coordination, and energy efficiency and PUE optimization.
  • Assess how future AI compute growth will influence utility planning and power infrastructure strategies.

Cooling & Thermal Management Technologies

  • Analyze current and emerging thermal management solutions including air cooling, direct-to-chip liquid cooling, immersion cooling, rear-door heat exchangers, and hybrid cooling architectures.
  • Assess implications of ultra-high-density AI deployments on mechanical system design, water usage, cooling scalability, heat rejection strategies, thermal resiliency, and future cooling infrastructure standards.
  • Evaluate cooling technologies and infrastructure requirements as AI rack densities continue to increase.

Vendor Engagement & Emerging Technology Assessment

  • Interface directly with technology vendors, OEMs, utilities, and strategic partners across power generation, UPS systems, electrical infrastructure, cooling technologies, liquid cooling platforms, AI compute infrastructure, and networking and optical interconnect technologies.
  • Lead technical assessments of next-generation technologies with respect to reliability, scalability, energy efficiency, sustainability, AI workload performance, construction complexity, and operational resiliency.
  • Support proof-of-concept initiatives, pilot deployments, and technology benchmarking efforts.
  • Develop executive-level recommendations regarding adoption of emerging AI infrastructure technologies and strategic engineering standards.

Cross-Functional Collaboration

  • Collaborate with design engineering, construction, operations, energy strategy, procurement, utilities, technology partners, and external engineering firms and consultants.
  • Support strategic planning initiatives and executive-level technical presentations.
  • Assist in developing future infrastructure standards and innovation roadmaps for AI-enabled data center platforms.
What makes you a good fit: (Qualifications)

Required

  • Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer Engineering, Computer Science, Data Center Engineering, or a related technical discipline.
  • 5+ years of experience in one or more of the following: data center infrastructure, AI/HPC infrastructure, power systems engineering, cooling technologies, or advanced infrastructure engineering.
  • Strong understanding of emerging AI compute technologies and infrastructure implications.
  • Ability to analyze complex technical systems and translate findings into actionable engineering and infrastructure strategies.

Preferred

  • Master's degree and/or PhD in Engineering, Computer Science, Data Science, Energy Systems, or a related technical field.
  • Experience with hyperscale or colocation data center environments.
  • Knowledge of GPU infrastructure and AI workload behavior.
  • Familiarity with utility coordination and energy systems.
  • Understanding of high-density cooling technologies.
  • Experience supporting large-scale infrastructure development projects.
  • PE license or equivalent advanced technical credentials.

***Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or transfer sponsorship of an employment visa at this time, including CPT/OPT.***

Benefits:
 
We offer a competitive benefit package for full-time employees that includes:
Medical, dental, and vision insurance
Company paid Life Insurance
Voluntary supplemental life insurance
Flexible spending account
Health savings account
Non-accrued PTO
Short- and Long-Term Disability
Company paid holidays
401k Retirement Plan with employer match
Mental Wellness Benefits
Tuition Reimbursement
Paid Parental Leave
Employee Discount Marketplace
Employee Recognition Platform
 
 
Individual compensation will be commensurate with the candidate's experience.
This position will also be eligible for an annual bonus.
NOTE: This job description is not intended to be all-inclusive. Employee may perform other related duties as assigned to meet the ongoing needs of the organization.

Cologix is proud to be an Equal Opportunity Employer. Qualified applicants are considered for employment without regard to age, race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or veteran status.  If you need assistance in applying for any of our open positions, please contact us at recruiting@cologix.com or call 720-940-2551. 

The California Consumer Privacy Act (“CCPA”) creates privacy rights relating to the collection, sale, disclosure, and deletion of consumers’ personal information. The CCPA requires businesses to provide consumers, including job applicants and employees, with information about their rights, including a description of the categories of personal information to be collected and the purpose for which the information will be used. For additional information regarding your rights, including a description of the categories of personal information to be collected and the purpose for which the information will be used, please see https://cologix.com/privacy-policy/.

Cologix’ data centers are ISO 27001:2022 and ISO 14001:2015 certified. These certifications demonstrate Cologix’s commitment to both information security and environmental stewardship. At Cologix, protecting information assets and minimizing environmental impacts are everyone’s responsibility.

Cologix employees are responsible for:
• Understanding and following Cologix’s information security, cybersecurity, privacy,
and environmental management policies, procedures, and standards.
• Ensuring conformance with the requirements of both the Information Security
Management System (ISMS) and the Environmental Management System (EMS).
• Remaining vigilant and reporting any information security or environmental incidents,
vulnerabilities, risks, or non-conformities to the appropriate teams.
• Actively participating in Cologix’s efforts to maintain and improve information security
and environmental performance.