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Internship Mechanical Engineering Machine Learning Jobs in California

HADRIAN

Machine Learning Engineer

Torrance, CA · On-site

$160K - $250K/yr

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling ... Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or similar ...

Apple Inc.

Machine Learning Engineer- GenAI

San Diego, CA · On-site

$150.40 - $277.60/hr

Masters in Artificial intelligence, Machine Learning, Computer Science, Statistics, Operations Research, Physics, Mechanical Engineering, Electrical Engineering or related field. Preferred ...

Apple

Machine Learning Data Engineer

Cupertino, CA · On-site

$141K - $169K/yr

Experience in data analysis, data engineering, and machine learning data operations. Experience designing data quality control processes, data curation workflows, or Human-in-the-Loop initiatives.

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine Learning Engineer Position: Full time Location: Carlsbad office About Us: NTENT provides a Platform-as-a-Service (PaaS), allowing industry partners to customize, localize and integrate search ...

Gotion, Inc.

Machine Learning Engineer

Fremont, CA

We lead the full product development cycle, integrating mechanical, electrical, thermal, and ... D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied ...

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All JobsMechanical Engineering Machine Learning JobsInternship Mechanical Engineering Machine Learning JobsInternship Mechanical Engineering Machine Learning Jobs in California

Internship Mechanical Engineering Machine Learning information

What is the difference between Internship Mechanical Engineering Machine Learning vs Mechanical Engineer?

AspectInternship Mechanical Engineering Machine LearningMechanical Engineer
Required CredentialsTypically pursuing or recently completed a degree in Mechanical Engineering or related field; familiarity with machine learning toolsBachelor's degree in Mechanical Engineering; professional licensure often not required for entry-level roles
Work EnvironmentInternship setting, often in research labs or tech companies, focusing on project-based learningDesign, analysis, and manufacturing environments, including offices, factories, and labs
Employer & Industry UsageUsed by tech companies, research institutions, and engineering firms exploring AI applications in mechanical systemsWidely employed across manufacturing, automotive, aerospace, and energy sectors

In summary, an Internship Mechanical Engineering Machine Learning focuses on gaining experience at the intersection of mechanical engineering and machine learning, often in research or tech environments. A Mechanical Engineer typically works on designing and maintaining mechanical systems across various industries, with a broader scope and more established credentials.

What are the most commonly searched types of Mechanical Engineering Machine Learning jobs in California? The most popular types of Mechanical Engineering Machine Learning jobs in California are:
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Internship Mechanical Engineering Machine Learning jobs near you

Senior Machine Learning Engineer

TetraMem - Accelerate The World

San Jose, CA • On-site

$122K - $168K/yr

Full-time

Re-posted 28 days ago

Job description

Job Summary:
TetraMem is a company focused on accelerating the world through innovative technology. They are seeking a Senior Machine Learning Engineer to develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly in audio processing, while providing technical leadership and mentoring to junior engineers.
Responsibilities:
• Develop, optimize, and deploy lightweight machine learning models for edge AI applications, particularly for audio processing.
• Implement and optimize ML models on embedded platforms, including FPGA and custom ASIC solutions.
• Work closely with hardware and software teams to integrate ML models into production systems.
• Research and implement state-of-the-art ML techniques to enhance model efficiency, latency, and power consumption for embedded AI applications.
• Improve inference efficiency and model compression techniques, including quantization, pruning, and knowledge distillation.
• Collaborate with cross-functional teams to drive innovation and contribute to the overall system architecture.
• Provide technical leadership and mentorship to junior engineers.
• Publish research findings, present at conferences, and contribute to open-source projects when applicable.
Qualifications:
Required:
• 5+ years of relevant industry experience (or a PhD) in Computer Science, Electrical Engineering, Machine Learning, or related fields.
• Must have prior experience managing a team, serving in a Team Lead role, or demonstrating strong technical leadership and cross-functional coordination capabilities.
• Strong hands-on experience in machine learning, with a focus on edge AI, on-device inference, and deploying lightweight models on resource-constrained devices.
• Expertise in modern ML frameworks such as PyTorch, TensorFlow (including TensorFlow Lite), and JAX.
• Proficiency in Python and C/C++, with practical experience in ML model optimization and production deployment.
• Deep experience with model quantization (PTQ/QAT), pruning, knowledge distillation, sparsity, and other compression techniques for efficient edge inference.
• Hands-on experience developing for or integrating with AI chip SDKs, neural accelerators (NPUs/DSPs), or hardware-specific toolchains (e.g., NVIDIA TensorRT, Qualcomm Neural Processing SDK, ARM Ethos, or similar).
• Familiarity with edge inference runtimes (ONNX Runtime, ExecuTorch, TVM) and optimizing models for hardware constraints (latency, memory footprint, power consumption).
Preferred:
• Understanding of ML compiler and runtime design.
• Experience working with tools such as Optimum, ONNX, TensorRT, TFLite/LiteRT, ncnn, or CoreML.
• Familiarity with hardware acceleration techniques.
• Experience in embedded system development.
Company:
TetraMem is developing cutting-edge analog computing solutions for AI applications, offering exceptional performance with ultra-low power consumption. Founded in 2018, the company is headquartered in Newark, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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