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Summer Machine Learning Hardware Jobs (NOW HIRING)

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

The Machine Learning Platform Technology team is building groundbreaking technology for search ... from our hardware. As part of this group, you will work with one of the most exciting high ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Summer 2027 Internships: Monday, May 17, 2027 (40/hours per week max) How You'll Be Rewarded Salary ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Summer 2027 Internships: Monday, May 17, 2027 (40/hours per week max) How You'll Be Rewarded Salary ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Summer 2027 Internships: Monday, May 17, 2027 (40/hours per week max) How You'll Be Rewarded Salary ...

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Summer 2027 Internships: Monday, May 17, 2027 (40/hours per week max) How You'll Be Rewarded Salary ...

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Summer Machine Learning Hardware information

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How much do summer machine learning hardware jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for summer 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 the difference between Summer Machine Learning Hardware vs Summer Data Scientist?

AspectSummer Machine Learning HardwareSummer Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related fields; knowledge of hardware design and programmingBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis and programming
Work EnvironmentHardware labs, R&D centers, tech companies focusing on AI hardwareOffice settings, research labs, tech companies analyzing data and building models
Industry UsageAI hardware development, embedded systems, hardware acceleration for MLData analysis, predictive modeling, AI application development

Summer Machine Learning Hardware roles focus on designing and optimizing hardware for machine learning applications, requiring technical skills in hardware engineering. In contrast, Summer Data Scientist positions involve analyzing data, building models, and deriving insights. Both roles are essential in AI development but differ in their technical focus and work environment.

What cities are hiring for Summer Machine Learning Hardware jobs?

Cities with the most Summer Machine Learning Hardware job openings:

What are the most commonly searched types of Machine Learning Hardware jobs?

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Summer Machine Learning Hardware jobs near you

Machine Learning System Hardware Architect

Sunnyvale, CA • On-site

Baidu USA
See more jobs at Baidu USAexternal

$285K/yr

Full-time

Re-posted yesterday

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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