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Machine Learning Hardware Jobs (NOW HIRING)

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

As part of our machine learning team, you will play a vital role in prototyping foundational machine learning tools that bridge the camera hardware and software, in order to build flawless camera ...

IMC Inc

Machine Learning Engineer

Dorchester, MA

$175K - $250K/yr

Machine Learning Engineer Chicago, United States; Hong Kong, Hong Kong; Sydney, Australia As a ... You'll collaborate with leading researchers, hardware experts, and software engineers to build ...

IMC Inc

Machine Learning Engineer

Chicago, IL

$175K - $250K/yr

Machine Learning Engineer Chicago, United States; Hong Kong, Hong Kong; Sydney, Australia As a ... You'll collaborate with leading researchers, hardware experts, and software engineers to build ...

IMC

Machine Learning Engineer

Chicago, IL · On-site

$175K - $250K/yr

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the ... You'll collaborate with leading researchers, hardware experts, and software engineers to build ...

Avride

Machine Learning Engineer

Austin, TX · On-site

Optimize inference performance, model compression, and deployment across various hardware platforms ... Strong understanding of fundamental machine learning algorithms and neural network techniques.

NR Consulting

Machine Learning Engineer

Fremont, CA · On-site

Work closely with hardware and software teams to integrate ML models into production systems ... Strong experience in machine learning, with a focus on edge AI and lightweight model deployment.

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How much do machine learning hardware jobs pay per hour?

As of Jun 2, 2026, the average hourly pay for machine learning hardware in the United States is $24.59, according to ZipRecruiter salary data. Most workers in this role earn between $17.55 and $27.88 per hour, depending on experience, location, and employer.

What is a Machine Learning Hardware job?

A Machine Learning Hardware job involves designing, optimizing, and developing specialized hardware to accelerate machine learning workloads. Professionals in this field work on hardware architectures like GPUs, TPUs, FPGAs, and custom accelerators to improve efficiency, performance, and power consumption. They collaborate with software engineers and data scientists to optimize hardware-software co-design. This role requires expertise in computer architecture, parallel computing, and low-level programming.

What are the key skills and qualifications needed to thrive in the Machine Learning Hardware position, and why are they important?

To thrive in Machine Learning Hardware, you need a solid background in computer engineering, digital design, and machine learning principles, often supported by a degree in electrical engineering, computer engineering, or a related field. Familiarity with hardware description languages (such as VHDL or Verilog), simulation tools, FPGA/ASIC development platforms, and possibly certifications in hardware design or ML accelerators is valuable. Collaboration, problem-solving, and the ability to communicate complex technical ideas effectively are essential soft skills. These skills enable you to design and optimize specialized hardware solutions that accelerate machine learning workloads and foster interdepartmental innovation.

What are the typical day-to-day responsibilities for a Machine Learning Hardware engineer?

As a Machine Learning Hardware engineer, your daily tasks often include collaborating with data scientists and software engineers to understand computational requirements, designing and prototyping hardware accelerators, and optimizing existing architectures for improved performance and efficiency. You might work with simulation tools to model new designs, validate hardware functionality, and troubleshoot issues during integration. The role typically involves both independent technical work and teamwork across hardware and AI/ML departments. This position requires keeping up to date with emerging technologies to ensure your solutions remain cutting-edge and competitive in the fast-evolving landscape of artificial intelligence.
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Machine Learning Hardware jobs near you
Infographic showing various Machine Learning Hardware job openings in the United States as of May 2026, with employment types broken down into 94% Full Time, and 6% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $51,154 per year, or $24.6 per hour.

Machine Learning System Hardware Architect

Baidu USA

Sunnyvale, CA

$285.20K/yr

Other

Posted 20 days ago

Job description

Do you want to be part of the AI revolution? Do you want to think out of the box, thriving on challenges in the AI industry and the desire to solve them? Do you want to work with a world-class team to explore the fast-growing AI hardware opportunities and impact on the AI industry?

We're looking forward to you joining us to collaborate, contribute, and revolutionize AI silicon and system.

Description

We are looking for a world-class Machine Learning System Architect (HW) to join our SoC team at Baidu's Sunnyvale office. The successful candidate will be a motivated self-starter who will thrive in this highly technical environment. Your job responsibilities as a Machine Learning System Architect will help the team to architect and create high-performance machine learning silicon and connect thousands of Kunlun Accelerators together for distributed AI training tasks.

Create differentiated architectural innovations for Baidu's Kunlun AI SoC roadmap. Architect, simulate, and design amazing machine learning solutions for our AI machine learning products.

Develop system-level ML architectures that push the boundaries of performance, power, and latency; collaborate closely with many other teammates to ensure we design and optimize hardware and software for maximum performance.

Monitor industrial and academic trends in artificial intelligence and determine where they should intersect our roadmaps. Drive partnerships for access to the most advanced AI technologies

Evaluate the power, performance, and cost of prospective architecture and subsystems. Build scalable tools for modeling and performance evaluation.

Engage with system and application software engineers to ensure optimization of the entire hardware/software stack.

Engage with SoC design, verification, and validation engineers to realize the architecture.

Qualifications

  • Knowledge of Machine Learning market, technological and business trends, software ecosystem, and emerging applications.
  • Proven track record 5+ years architecting hardware solutions for Machine Learning, acceleration and optimization.
  • Experience with deep learning frameworks including TensorFlow, PyTorch, PaddlePaddle, etc.
  • Strong track record of outreach to ML researchers and application developers.
  • Experience with CPUs, GPUs, memory systems, and accelerators.
  • Experience with performance simulation and modeling in C++
  • Experience with SoC interconnects and NoCs
  • Experience with area, frequency, and power optimizations
  • Familiarity with video, DSP, Ethernet, and PCIe
  • MS or PhD in Electrical or Computer Engineering.
  • Excellent communication skills in both English and Chinese.

Culture Fit:

  • Mission alignment: If you want to be part of a team to accomplish this great mission, we will provide you the best possible platform to do that.
  • Self-directed: We work best with people that are driven, motivated, and aspire to greatness.
  • Hungry to learn: We are eager to see you learn new skills and grow.
  • Team orientation: We work in small, fast-moving teams. We watch out for each other and go after big goals together as a team.

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